Video Object Detection with an Aligned Spatial-Temporal Memory

We introduce Spatial-Temporal Memory Networks for video object detection. At its core, a novel Spatial-Temporal Memory module (STMM) serves as the recurrent computation unit to model long-term temporal appearance and motion dynamics. The STMM’s design enables full integration of pretrained backbone CNN weights, which we find to be critical for accurate detection. Furthermore, in order to tackle object motion in videos, we propose a novel MatchTrans module to align the spatial-temporal memory from frame to frame. Our method produces state-of-the-art results on the benchmark ImageNet VID dataset, and our ablative studies clearly demonstrate the contribution of our different design choices. We release our code and models at http://fanyix.cs.ucdavis.edu/project/stmn/project.html.

[1]  David Beymer,et al.  A real-time computer vision system for vehicle tracking and traffic surveillance , 1998 .

[2]  Yang Li,et al.  Reliable Patch Trackers: Robust visual tracking by exploiting reliable patches , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Daniel Cremers,et al.  Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking , 2017, ArXiv.

[5]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[6]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[7]  Xiaogang Wang,et al.  DeepID-Net: Deformable deep convolutional neural networks for object detection , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Vibhav Vineet,et al.  Conditional Random Fields as Recurrent Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[9]  Cordelia Schmid,et al.  Human Detection Using Oriented Histograms of Flow and Appearance , 2006, ECCV.

[10]  Andrew G. Howard,et al.  Some Improvements on Deep Convolutional Neural Network Based Image Classification , 2013, ICLR.

[11]  Shuicheng Yan,et al.  Seq-NMS for Video Object Detection , 2016, ArXiv.

[12]  Xiaogang Wang,et al.  Object Detection from Video Tubelets with Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Daniel Snow,et al.  Pedestrian detection using boosted features over many frames , 2008, 2008 19th International Conference on Pattern Recognition.

[14]  Jitendra Malik,et al.  Human Pose Estimation with Iterative Error Feedback , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Truong Q. Nguyen,et al.  Context Matters: Refining Object Detection in Video with Recurrent Neural Networks , 2016, BMVC.

[17]  Koray Kavukcuoglu,et al.  Multiple Object Recognition with Visual Attention , 2014, ICLR.

[18]  Andrew Zisserman,et al.  Detect to Track and Track to Detect , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[19]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[20]  Thomas Brox,et al.  FlowNet: Learning Optical Flow with Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[21]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[23]  Konrad Schindler,et al.  Online Multi-Target Tracking Using Recurrent Neural Networks , 2016, AAAI.

[24]  Xiaolin Hu,et al.  Recurrent convolutional neural network for object recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Christopher Joseph Pal,et al.  Delving Deeper into Convolutional Networks for Learning Video Representations , 2015, ICLR.

[26]  Karteek Alahari,et al.  Learning Video Object Segmentation with Visual Memory , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[27]  Bin Yang,et al.  CRAFT Objects from Images , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Wei Xu,et al.  Explain Images with Multimodal Recurrent Neural Networks , 2014, ArXiv.

[29]  Silvio Savarese,et al.  Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[30]  Deva Ramanan,et al.  Exploring Weak Stabilization for Motion Feature Extraction , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Trevor Darrell,et al.  Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Bohyung Han,et al.  Learning Multi-domain Convolutional Neural Networks for Visual Tracking , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[34]  Xiaogang Wang,et al.  Object Detection in Videos with Tubelet Proposal Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2005, International Journal of Computer Vision.

[36]  Xiaogang Wang,et al.  T-CNN: Tubelets With Convolutional Neural Networks for Object Detection From Videos , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[37]  Jitendra Malik,et al.  Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[39]  Geoffrey E. Hinton,et al.  Layer Normalization , 2016, ArXiv.

[40]  Zhe Chen,et al.  MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[41]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[42]  Alex Graves,et al.  Recurrent Models of Visual Attention , 2014, NIPS.

[43]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[44]  Abhinav Gupta,et al.  Contextual Priming and Feedback for Faster R-CNN , 2016, ECCV.

[45]  Yoshua Bengio,et al.  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.

[46]  Andrew Zisserman,et al.  Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.

[47]  Ronan Collobert,et al.  Learning to Segment Object Candidates , 2015, NIPS.

[48]  Serge J. Belongie,et al.  Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[49]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[50]  Yujie Wang,et al.  Flow-Guided Feature Aggregation for Video Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[51]  Jitendra Malik,et al.  Recurrent Network Models for Human Dynamics , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[52]  Samy Bengio,et al.  Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[53]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[54]  Abhinav Gupta,et al.  Training Region-Based Object Detectors with Online Hard Example Mining , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[55]  Xinlei Chen,et al.  Spatial Memory for Context Reasoning in Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[56]  Xiaogang Wang,et al.  Visual Tracking with Fully Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[57]  Ruslan Salakhutdinov,et al.  Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models , 2014, ArXiv.

[58]  Yichen Wei,et al.  Deep Feature Flow for Video Recognition , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[59]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[60]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[61]  Lorenzo Torresani,et al.  Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[62]  Yichen Wei,et al.  Towards High Performance Video Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[63]  Enkhbayar Erdenee,et al.  Multi-class Multi-object Tracking Using Changing Point Detection , 2016, ECCV Workshops.

[64]  Xiaogang Wang,et al.  DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection , 2014, ArXiv.

[65]  Bernt Schiele,et al.  New features and insights for pedestrian detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.