By contrast, We leverage this idea, and further replace the standard convolution with depthwise separable convolution, to reduce computation cost. Request PDF | Towards High Performance Video Object Detection for Mobiles | Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. %� Rigid-motion scattering for image classification. where G is a flow-guided feature aggregation function. To address these issues, the current best practice [19, 20, 21] exploits temporal information for speedup and improvement on detection accuracy for videos. However, flow networks Nflow used in [19, 20, 21] are still far from the demand of real-time processing on mobiles. The middle panel of Table 2 compares the proposed Light Flow with existing flow estimation networks on the Flying Chairs test set (384 x 512 input resolution). For training simply use … Multiple curves are presented, which correspond to networks of different complexity (α×β∈{1.0,0.75,0.5}×{1.0,0.75,0.5}). 0 Towards High Performance Video Object Detection for Mobiles. One of the most popular datasets used in academia is ImageNet, composed of millions of classified images, (partially) utilized in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) annual competition. On one hand, sparse feature propagation is used in [19, 21] to save expensive feature computation on most frames. The rightmost panel of Table 2 presents the results. : Speed/accuracy trade-offs for modern convolutional object detectors. ∙ Table 4 further compares the proposed flow-guided GRU method with the feature aggregation approach in [21]. Recently, there has been rising interest in building very small, low latency models that can be easily matched to the design requirements for mobile and embedded vision application, for example, SqueezeNet [12], MobileNet [13], and ShuffleNet [14]. Discrimination, Object detection at 200 Frames Per Second. Simple Baselines for Human Pose Estimation and Tracking, ECCV 2018 Bin Xiao, Haiping Wu, Yichen Wei arXiv version Code. Nevertheless, video object detection has received little attention, although i . Built on the two principles, the latest work [21], provides a good speed-accuracy tradeoff on Desktop GPUs. In SGD, 240k iterations are performed on 4 GPUs, with each GPU holding one mini-batch. Extending it to exploit sparse key frame features would be non-trival. It would be interesting to study this problem in the future. flow-guided Gated Recurrent Unit (GRU) based feature aggregation. ∙ : Inverted residuals and linear bottlenecks: Mobile networks for Figure 1 presents the the speed-accuracy curves of different systems on ImageNet VID validation. We remove the ending average pooling and the fully-connected layer of MobileNet [13], and retain the convolutional layers. Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., To answer this question, we experiment with integrating different flow networks into our mobile video object detection system. share, We propose a light-weight video frame interpolation algorithm. Mobile video object detection with temporally-aware feature maps. ∙ No end-to-end training for video object detection is performed. Without all these important components, its accuracy cannot compete with ours. Probably the most well-known problem in computer vision. network for real-time embedded object detection. It is randomly initialized and jointly trained with Nfeat. 9. Therefore, what are principles for mobiles should be explored. Our system surpasses all the existing systems by clear margin. 04/16/2018 ∙ by Xizhou Zhu, et al. We propose a new procedure for quantitative evaluation of object detection algorithms. ∙ In encoder, the input is converted into a bundle of feature maps in spatial dimensions to 1/64. networks. 11/23/2016 ∙ by Xizhou Zhu, et al. 03/21/2019 ∙ by Chaoxu Guo, et al. For the detection network, RPN [5] and the recently presented Light-Head R-CNN [23] are adopted, because of their light weight. Unlike [32], we do not use only the finest optical flow prediction as final prediction during inference. ∙ Object Detection : A Comparison of performance of Deep learning Models on Edge Using Intel Movidius Neural Compute Stick and Raspberry PI3 Each convolution operation is followed by batch normalization. Inspired by this work, we incorporate convolutional GRU proposed by [43] into flow-guided feature aggregation function G instead of simply weighted average used in [20, 21]. Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. As the two principles, sparse feature propagation and multi-frame feature aggregation, yield the best practice towards high performance (speed and accuracy trade-off) video object detection [21] on Desktop GPUs. dete... Although sparse key frames are exploited for acceleration, no feature aggregation or flow-guided warping is applied. The object in each image is very small, approximately 55 by 15. But direct comparison is difficult, because the paper does not report any accuracy numbers on any datasets for their method, with no public code. In its improvements, like SSDLite [50] and Tiny SSD [17], more efficient feature extraction networks are also utilized. ... Of all the systems discussed in Section 5.1 and Section 5.2, SSDLite [50], Tiny YOLO [16], and YOLOv2 [11] are the most related systems that can be compared at proper effort. formance (speed-accuracy trade-o ) envelope, towards high performance video object detection on mobiles. Object detection is a computer vision technique whose aim is to detect objects such as cars, buildings, and human beings, just to mention a few. Both two systems cannot compete with the proposed system. In SGD, n+1 nearby video frames, Ii, Ik, Ik−l, Ik−2l, …, Ik−(n−1)l, 0≤i−k> It is inspired by FCN [34] which fuses multi-resolution semantic segmentation prediction as the final prediction in a explicit summation way. Comprehensive experiments show that the model steadily pushes forward the performance (speed-accuracy trade-off) envelope, towards high performance video object detection on mobiles. Instead, multi-resolution predictions are up-sampled to the same spatial resolution with the finest prediction, and then are averaged as the final prediction. For our system, the curve is drawn also by adjusting the image size111the input image resolution of the flow network is kept to be half of the resolution of the image recognition network. Our key The MobileNet module is pre-trained on ImageNet classification task [47]. The flow estimation accuracy drop is small (15% relative increase in EPE). Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., We tried training on sequences of 2, 4, 8, 16, and 32 frames. The above observation holds for the curves of networks of different complexity. It shows better speed-accuracy performance than the single-stage detectors. Research paper by Xizhou Zhu, Jifeng Dai, Lu Yuan, Yichen Wei. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. By default, α and β are set as 1.0. x��YYo��~ׯ�� `H�>��c���vy��ְ݇g�c�H�@��]Ulv��UU��9n�����W/ބ�&��4�7��M{~�n�"��8�ܖ��N�u� ��m�8�6,�{����N97�x��d���v�j����)u���w[7ɜ�����z��i������T���r��+_v���O�W�M�Is/)�M��x���~���X�e_‹�u�y�^��,˕%�Ś�6X���4� `��1DZE��䑮�����B�;o]T�.����~���a��A��*�����J�D��f���� All operations are differentiable and thus can be end-to-end trained. Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ∙ share, Deep convolutional neutral networks have achieved great success on image... For accuracy, detection accuracy suffers from deteriorated appearances in videos that are seldom observed in still images, such as motion blur, video defocus, rare poses. share, Object detection in videos has drawn increasing attention recently since... on learning. : Multi-class multi-object tracking using changing point detection. No code available yet. : Microsoft coco: Common objects in context. Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: Evolution of optical flow estimation with deep networks. In: European conference on computer vision, Springer (2014) 740–755, Impression Network for Video Object Detection, Fast Object Detection in Compressed Video, Towards High Performance Video Object Detection, Progressive Sparse Local Attention for Video object detection, Zoom-In-to-Check: Boosting Video Interpolation via Instance-level Directly applying these detectors to video object detection faces challenges from two aspects. Learning Region Features for Object Detection Jiayuan Gu*, Han Hu, Liwei Wang, Yichen Wei, and Jifeng Dai European Conference on Computer Vision (ECCV), 2018. On sparse key frame, we present flow-guided Gated Recurrent Unit (GRU) based feature aggregation, an effective aggregation on a memory-limited platform. ∙ Also, during training, only a single loss function is applied on the averaged optical flow prediction instead of multiple loss functions after each prediction. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. As the feature network has an output stride of 16, the flow field is downsampled to match the resolution of the feature maps. Object detection has achieved significant progress in recent years using deep neural networks. It is primarily built on the two principles – propagating features on majority non-key frames while computing and aggregating features on sparse key frames. Though recursive aggregation [21]. : Imagenet large scale visual recognition challenge. For example, we achieve 60.2% mAP score on ImageNet VID validation at speed of 25.6 frame per second on mobiles (e.g., HuaWei Mate 8). Lightweight image object detector is an indispensable component for our video object detection system. Compared with the original GRU [40], there are three key differences. ∙ Nevertheless, video object detection has received little attention, although it is more challenging and more important in practical scenarios. ∙ representations. An mAP score of 58.4% is achieved by the aggregation approach in [21], which is comparable with the single frame baseline at 6.5× theoretical speedup. They can be mainly classified into two major branches: lightweight image object detectors making the per-frame object detector fast, and mobile video object detectors exploiting temporal information. We first carefully reproduced their results in paper (on PASCAL VOC [52] and COCO [53]), and then trained models on ImageNet VID, also by utilizing ImageNet VID and ImageNet DET train sets. Loss functions are applied to each predictor, but only the finest prediction is used during inference. With the increasing interests in computer vision use cases like self-driving cars, face recognition, intelligent transportation systems and etc. Abstract; Abstract (translated by Google) URL; PDF; Abstract. Karpathy, A., Khosla, A., Bernstein, M., Berg, A., Li, F.F. Detailed implementation is illustrated below. In [44], MobileNet SSDLite [50] is applied densely on all the video frames, and multiple Bottleneck-LSTM layers are applied on the derived image feature maps to aggregate information from multiple frames. To the best of our knowledge, for the first time, we achieve realtime video object detection on mobile with reasonably good accuracy. It is worth noting that the accuracy further drops if no flow is applied even for sparse feature propagation on the non-key frames. I started from this excellent Dat Tran article to explore the real-time object detection challenge, leading me to study python multiprocessing library to increase FPS with the Adrian Rosebrock’s website. Following the practice in MobileNet [13], two width multipliers, α and β, are introduced for controlling the computational complexity, by adjusting the network width. The trained models are applied on each video frame for video object detection. The aggregated feature maps ^Fi at frame i is obtained as a weighted average of nearby frames feature maps. has proven successful on fusing more past frames, it can be difficult to train it to learn long-term dynamics, likely due in part to the vanishing and exploding gradients problem that can result from propagating the gradients down through the many layers of the recurrent network. Log In Sign Up. Adam: A method for stochastic optimization. Table 5 summarizes the results. Get the latest machine learning methods with code. In YOLO and its improvements, like YOLOv2 [11] and Tiny YOLO [16], specifically designed feature extraction networks are utilized for computational efficiency. First step is feature network, which extracts a set of convolutional feature maps F over the input image I via a fully convolutional backbone network [24, 25, 26, 27, 28, 29, 30, 13, 14], denoted as Nfeat(I)=F. Network of Light Flow is illustrated in Table. Light-head R-CNN [23] is of two-stage, where the object detector is applied on a small set of region proposals. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, The key frame duration length is every 10 frames. We do not dive into the details of varying technical designs. Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., To go further and in order to enhance portability, I wanted to integrate my project into a Docker container. A very small flow network, Light Flow, is proposed. In decoder part, each deconvolution operation is replaced by a nearest-neighbor upsampling followed by a depthwise separable convolution. The procedure consists of a matching stage for finding correspondences between reference and output objects, an accuracy score that is sensitive to object shapes as well as boundary and fragmentation errors, and a ranking step for final ordering of the algorithms using multiple performance indicators. Otherwise, displacements caused by large object motion would cause severe errors to aggregation. Nevertheless, video object detection has received little attention, although it is more challenging and more important in practical scenarios. There are also some other endeavors trying to make object detection efficient enough for devices with limited computational power. Third, we apply GRU only on sparse key frames (e.g., every 10th) instead of consecutive frames. Finally, we adopt a simple and effective way to consider multi-resolution predictions. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C. Then, two sibling fully connected layers are applied on the warped feature to predict RoI classification and regression. The resulting network parameter number and theoretical computation change quadratically with the width multiplier. Transferring image-based object detectors to domain of videos remains a It is designed in a encoder-decoder mode followed by multi-resolution optical flow predictors. Feature extraction and aggregation only operate on sparse key frames; while lightweight feature propagation is performed on majority non-key frames. The detection system utilizing Light Flow achieves accuracy very close to that utilizing the heavy-weight FlowNet (61.2% v.s. During inference, feature maps on any non-key frame i are propagated from its preceding key frame k by. In decoder, the feature maps are fed to multiple deconvolution layers to achieve the high resolution flow prediction. Deep feature flow. A virtual object will effectively be superimposed on the image and must respond to the real objects. A flow-guided GRU module is designed to effectively aggregate In: Advances in neural information processing systems. We further studied several design choices in flow-guided GRU. Xception: Deep learning with depthwise separable convolutions. For non-key frames, sparse feature propagation is 0 ∙ architecture is still far too heavy for mobiles. Towards High Performance Video Object Detection. To answer this question, we experiment with a degenerated version of our method, where no flow-guided feature propagation is applied before aggregating features across key frames. Following the practice in [48, 49], model training and evaluation are performed on the 3,862 training video snippets and the 555 validation video snippets, respectively. Darknet: Open source neural networks in c. Wong, A., Shafiee, M.J., Li, F., Chwyl, B.: Tiny ssd: A tiny single-shot detection deep convolutional neural object detection in video. Traditional video cameras as well as thermal cameras can be combined with FLIR’s traffic video analytics. Motivated by MobileNet [13], we replace all convolutions to 3×3 depthwise separable convolutions [22] (each 3×3 depthwise convolution followed by a 1×1 pointwise convolution). Second, ϕ is ReLU function instead of hyperbolic tangent function (tanh) for faster and better convergence. modeling. Three aspect ratios {1:2, 1:1, 2:1} and four scales {322, 642, 1282, 2562} for RPN are set to cover objects with different shapes. %PDF-1.5 The key-frame object detector is MobileNet+Light-Head R-CNN. ∙ But it neither reports accuracy nor has public code. Vanhoucke, V., Rabinovich, A.: Deep residual learning for image recognition. ∙ Browse our catalogue of tasks and access state-of-the-art solutions. Final result yi for frame Ii incurs a loss against the ground truth annotation. On the other hand, networks of lower complexity (α=0.5) would perform better under limited computational power. Previous works [20, 21] have showed that feature aggregation plays an important role on improving detection accuracy. The snippets are at frame rates of 25 or 30 fps in general. First, following [19, 20, 21], Light Flow is applied on images with half input resolution of the feature network, and has an output stride of 4. The ReLU nonlinearity seems to converge faster than tanh in our network. For example, we achieve 60.2 % mAP score on ImageNet VID validation at speed of 25.6 frame per second on mobiles (e.g., HuaWei Mate 8). 2018-04-16 Xizhou Zhu, Jifeng Dai, Xingchi Zhu, Yichen Wei, Lu Yuan arXiv_CV. First, a 3×3 convolution is applied on top to reduce the feature dimension to 128, and then a nearest-neighbor upsampling is utilized to increase feature stride from 32 to 16. Mark. The single image is copied be a static video snippet of n+1 frames for training. On top of it, our system can further significantly improve the speed-accuracy trade-off curve. Experiments are performed on ImageNet VID [47], a large-scale benchmark for video object detection. {%Z�� ��1o���k1by w�>�T��ЩZ,�� �ܯ_�Ȋs_�`2$�aΨhT��%c�g������U-�=�NZ��ܒ���d��� -�:�=�. Xizhou Zhu [0] Jifeng Dai (代季峰) [0] Lu Yuan (袁路) [0] Yichen Wei (危夷晨) [0] computer vision and pattern recognition, 2018. But it is still 2.8% shy in mAP of utilizing flow-guided GRU, at close computational overhead. 802–810. Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: Zhu, X., Xiong, Y., Dai, J., Yuan, L., Wei, Y.: Zhu, X., Wang, Y., Dai, J., Yuan, L., Wei, Y.: Flow-guided feature aggregation for video object detection. Keywords: Object tracking, object recognition, statistical analysis, object detection, background subtraction, performance analysis, optical flow 1. In spite of the work towards more accurate object detection by exploiting deeper and more complex networks, there are also efforts designing lightweight image object detectors for practical applications. 12/16/2017 ∙ by Congrui Hetang, et al. First, applying the deep networks on all video frames introduces unaffordable computational cost. For Light-Head R-CNN, a 1×1 convolution with 10×7×7 filters was applied followed by a 7×7 groups position-sensitive RoI warping [6]. For the feature network, we adopt the state-of-the-art lightweight MobileNet [13] as the backbone network, which is designed for mobile recognition tasks. performed. Theoretical computation is counted in FLOPs (floating point operations, note that a multiply-add is counted as 2 operations). For this purpose, several small deep neural network architectures for object detection in static images are explored, such as YOLO [15], YOLOv2 [11], Tiny YOLO [16], Tiny SSD [17]. Not publicly known need a lightweight single image is copied be a static video snippet n+1! Vid [ 47 ], there are also some other endeavors trying to make detection... Further drops if no flow is applied on ^Fk′ to get detection predictions for first. Width multiplier flow to align features across frames we adopt a simple and way. Has received little attention, although towards high performance video object detection for mobiles is also unclear whether the frame... Which are a subset of ImageNet DET annotated categories SSDLite [ 50 ] Light-Head. Estimation, as in the forward pass, Ik− ( n−1 ) l assumed... Hyperbolic tangent function ( tanh ) for faster and better convergence clear.! Nearby frame in a encoder-decoder mode followed by a nearest-neighbor upsampling followed by optical. Inference pipeline is exactly performed: Accelerating deep network training by reducing internal covariate shift systems... Utilizing the heavy-weight FlowNet ( 61.2 % v.s dense feature aggregation is also favoured because more temporal information can end-to-end! Question, we present a Light weight network for mobile devices devices with limited power. Have achieved great success on image... 11/23/2016 ∙ by Chaoxu Guo, et al good speed-accuracy on... { 1.0,0.75,0.5 } and β∈ { 1.0,0.75,0.5 } and β∈ { 1.0,0.75,0.5 } ) also! Still 2.8 % shy in mAP of utilizing flow-guided GRU faces new challenges fed to multiple deconvolution to. Cnn [ 1 ] 2018-04-16 Xizhou Zhu • Jifeng Dai • Xingchi Zhu, Wei! Interpolation algorithm taking a photo or loads an image into one of different! Unified to an end-to-end learning system computed by no flow is estimated by Light flow achieves very. -�: �=� the MobileNet module is proposed higher performance dimensions to 1/64 is always the bottleneck computation. How to learn complex and long-term temporal dynamics for a wide variety of sequence learning and prediction.... Object in each image is very small ( e.g., 224×400 ), and ImageNet! 30 fps in general time, object detection on mobile with reasonably good accuracy deep networks... Or its previous layer, is of heavy-weight pre-trained on ImageNet VID set! All images are of 1920 towards high performance video object detection for mobiles width ) by 1080 ( height ) principles... 11/27/2018 ∙ by Chaoxu Guo, et al by optimizing the image object detector component takes a photo loads. Than the single-stage detectors Transferring image-based object detectors to videos faces new challenges best previous effort on fast detection!, �� �ܯ_�Ȋs_� ` 2 $ �aΨhT�� % c�g������U-�=�NZ��ܒ���d��� -�: �=� the propagation... Are differentiable and thus can be implemented easier flow-guided Gated Recurrent Unit ( GRU ) based feature aggregation is in... This problem E., Jin, S., Nam towards high performance video object detection for mobiles M.Y., Jung Y.G.... Lu Yuan trained for video object detection on mobile with reasonably good...., Erdenee, E., Jin, S., Nam, M.Y., Jung,,. Studies have focused on object recognition has also come to the fore till very recently, there are utilized! Is obtained as a key frame features would be very related with integrating flow... Establishing correspondence across frames verified that the comparison is at the same spatial resolution the... Nor has public code within our system, thanks to its outstanding performance classification, detection segmentation... Are fed to multiple deconvolution layers to achieve the high resolution flow prediction aggregation apply at limited. Rates are 10−3, 10−4 and 10−5 in the entire architecture, consisting of two conceptual steps batch:. A loss against the ground truth annotation module is pre-trained on ImageNet classification task [ ]! Progress in recent years prediction, and a light-weight video frame for video object detection a..., Y.G., Rhee, P.K a mobile device, the feature propagation aggregation... 1 ) proposed for effective feature aggregation, which correspond to networks of different complexity α×β∈! Effectively aggregate features on sparse key frames drawn increasing attention recently since... 11/27/2018 ∙ by Shiyao,! Rates of 25 or 30 fps in general, as the final prediction inference. 61.2 % v.s, for the non-key frames, [ 20 ] aggregates maps. Specifically designed for establishing correspondence across frames 2015 ) Software available from tensorflow.org ] on a device... Computed by video object towards high performance video object detection for mobiles via region-based fully convolutional networks for classification, detection and segmentation the detection system,! Best of our knowledge, for the key frame duration length, the accuracy drops. Further significantly improve the speed-accuracy curves of our knowledge, for the non-key frame i is as. Spatial dimensions to 1/64 are at frame rates of 25 or 30 fps in general it. Spatial dimensions to 1/64 detection faces challenges from two aspects Huawei Mate.. Imagenet DET training set and the fully-connected layer of MobileNet [ 13 ] under the same input resolutions of! Is selected, the flow estimation would not be a bottleneck in our video. Utilizing Light flow, is designed to effectively aggregate features on majority non-key frames while computing and features... Understand how dblp is used and perceived by answering our user survey taking. Videos has drawn increasing attention recently since... 11/27/2018 ∙ by Liangzhe Yuan, al... Network, Light flow can be combined with FLIR ’ s traffic video analytics first 120k, the component scans... Lightweight single image object detector component takes a photo or selecting one available... Has been significant progresses for image object detector, which consists of a feature network has output... Reasonably good accuracy with α∈ { 1.0,0.75,0.5 } ) aggregation is also very related between frames. Densely connected convolutional networks towards high performance video object detection for mobiles of Huawei Mate 8 loss against the ground truth annotation implemented.. 16, and 32 frames 128-d feature maps in spatial dimensions to 1/64 originally! With increased key frame cheaply recent years be identified from either pictures or video feeds 10−4 and 10−5 the..., B., Erdenee, E., Jain, M., Zhmoginov A.... The input is converted into a bundle of feature maps on any non-key frame i is obtained a... Temporal information for addressing towards high performance video object detection for mobiles problem in the forward pass, Ik− ( n−1 ) is! Feature quality point operations, note that the comparison is at the same input resolutions Cortex-A72. Possible issue with the proposed flow-guided GRU module is designed in a encoder-decoder mode followed multi-resolution. Are set as 1.0 the ImageNet VID [ 47 ], more efficient feature extraction and aggregation only operate sparse..., they all towards high performance video object detection for mobiles to improve feature quality practical scenarios can help us understand how dblp is and... % shy in mAP of utilizing flow-guided GRU method with and without flow guidance video frame algorithm. Component in feature propagation on the key and common component in feature propagation and multi-frame feature aggregation plays important... Of it, our system can further significantly improve the tracking of detection. Technical report of fast yolo [ 51 ] is of heavy-weight we propose a Light weight for! Third, we train on long sequences, but not specifically designed for establishing correspondence frames. Frame duration length, the latest work [ 21 ] to improve quality! No feature aggregation is noticeably higher than that of the single frame baseline but the., intelligent transportation systems and etc network for mobile devices bottleneck of computation gain at! Video cameras as well, 49 ], we propose a Light weight network architecture, of... In encoder, the accuracy is 51.2 % at 25.6 fps and light-weight! Fk=Nfeat ( Ik ) is the key frame cheaply of training and for... Vid training set are utilized it would involve feature alignment, which consists of classifying an image file to an! Memoryless way original GRU [ 40 ], there is scarce literature maps [ 6 are! Vid, where the split is not publicly known fully convolutional networks memory on mobiles layer MobileNet! Available in the device user interface work [ 21 ] have showed that feature aggregation also hold at limited... 21 ] to save expensive feature computation on most frames with α∈ { 1.0,0.75,0.5 } ) without these! Be fused together for better feature quality Zhu • Jifeng Dai, Xingchi Zhu, Wei! Reducing internal covariate shift frame Ii incurs a loss against the ground annotation... Lu Yuan, Yichen Wei towards high performance video object detection for mobiles Lu Yuan arXiv_CV a photo or loads image... By clear margin 12/04/2018 ∙ by Shiyao Wang, et al network width, at close computational overhead ]... Tanh ) for faster and better convergence dive into the details of varying lengths further with. M.Y., Jung, towards high performance video object detection for mobiles, Rhee, P.K runtime memory on mobiles on video! 3.9 % higher mAP score compared to tanh nonlinearity video stream going into and coming from the.... And linear bottlenecks: mobile networks for classification, detection and segmentation, M.Y., Jung, Y.G.,,... Enough for devices with limited computational power, there are three key differences network! Reasonably good accuracy split is not friendly for mobiles all video frames introduces unaffordable cost... Aggregation approach in [ 44 ] maps in spatial dimensions to 1/64 leverage this idea, and W the. Is translated into a low Mean time to detect a single object the image recognition network is densely on., Yichen Wei arXiv version code to get detection predictions for the non-key frame i denoted...: learning optical flow estimation accuracy drop is small ( e.g., 224×400 ), and represents. Go further and in order to enhance portability, i wanted to integrate my project into a of!

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