Leveraging weak segmentation for multi-object tracking system

Object Tracking is an important task in Computer Vision, which has gained increasing attention from academia to industry. In this paper, we propose a real-time tracking system based on weak segmentation. Different from general tracking by detection systems, we do not classify objects into car, cat or bike, instead we just classify the image into object area and non-object area. Many tracking systems simply combine weak segmentation results with Kalman filter, which cannot track objects well when the target moves irregularly. In this paper, we use state-of-art background subtraction methods to accomplish weak segmentation and incorporates weak segmentation results with the local invariant feature to create robust object association, finally we proposal a status machine to convert temporary object association into long-term object tracking results. The experiment shows our algorithm outperforms the state-of-art tracking by weak segmentation algorithms.

[1]  Guillaume-Alexandre Bilodeau,et al.  Improving background subtraction using Local Binary Similarity Patterns , 2014, IEEE Winter Conference on Applications of Computer Vision.

[2]  Rui Wang,et al.  Static and Moving Object Detection Using Flux Tensor with Split Gaussian Models , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[3]  Gerhard Rigoll,et al.  A Deep Convolutional Neural Network for Background Subtraction , 2017, ArXiv.

[4]  Guillaume-Alexandre Bilodeau,et al.  Urban Tracker: Multiple object tracking in urban mixed traffic , 2014, IEEE Winter Conference on Applications of Computer Vision.

[5]  Pierre Vandergheynst,et al.  FREAK: Fast Retina Keypoint , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Rainer Stiefelhagen,et al.  Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics , 2008, EURASIP J. Image Video Process..

[7]  Guillaume-Alexandre Bilodeau,et al.  Flexible Background Subtraction with Self-Balanced Local Sensitivity , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[8]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Atsushi Shimada,et al.  Background model based on intensity change similarity among pixels , 2013, The 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision.

[10]  Anton van den Hengel,et al.  High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks , 2016, ArXiv.

[11]  Pietro Perona,et al.  Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Massimo De Gregorio,et al.  Change Detection with Weightless Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[14]  Zoran Zivkovic,et al.  Improved adaptive Gaussian mixture model for background subtraction , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[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]  Ramakant Nevatia,et al.  Robust Object Tracking by Hierarchical Association of Detection Responses , 2008, ECCV.

[17]  Xiaoxiao Li,et al.  Deep Learning Markov Random Field for Semantic Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Yashpal SinghA,et al.  Implementation of a Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications , 2010 .

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

[20]  Frédéric Lerasle,et al.  A comparative view on exemplar ‘tracking-by-detection’ approaches , 2015, 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

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

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

[23]  Bin Wang,et al.  A Fast Self-Tuning Background Subtraction Algorithm , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[24]  Luc Van Gool,et al.  Robust tracking-by-detection using a detector confidence particle filter , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[25]  Fatih Murat Porikli,et al.  Changedetection.net: A new change detection benchmark dataset , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[26]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[27]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Moongu Jeon,et al.  A New Framework for Background Subtraction Using Multiple Cues , 2012, ACCV.

[29]  Simone Calderara,et al.  Visual Tracking: An Experimental Survey , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Thierry Bouwmans,et al.  Traditional and recent approaches in background modeling for foreground detection: An overview , 2014, Comput. Sci. Rev..

[31]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[32]  Iasonas Kokkinos,et al.  Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.