A Camera Network Tracking (CamNeT) Dataset and Performance Baseline

In this paper, we propose a novel Non-Overlapping Camera Network Tracking Dataset (CamNeT) for evaluating multi-target tracking algorithms. The dataset is composed of five to eight cameras covering both indoor and outdoor scenes at a university. This dataset consists of six scenarios. Within each scenario are challenges relevant to lighting changes, complex topographies, crowded scenes, and changing grouping dynamics. Persons with predefined trajectories are combined with persons with random trajectories. Ground truth data for predefined trajectories is provided for each camera. Also, a baseline multi-target tracking system is presented. The tracking results using the baseline system are provided, which can be compared with future works. The work provides a comprehensive multicamera dataset for performance evaluation in this challenging application domain, as well as an initial set of results.

[1]  Mubarak Shah,et al.  Modeling inter-camera space-time and appearance relationships for tracking across non-overlapping views , 2008, Comput. Vis. Image Underst..

[2]  Bir Bhanu,et al.  VideoWeb Dataset for Multi-camera Activities and Non-verbal Communication , 2011 .

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

[4]  Ramakant Nevatia,et al.  Inter-camera Association of Multi-target Tracks by On-Line Learned Appearance Affinity Models , 2010, ECCV.

[5]  Ramakant Nevatia,et al.  Multi-target tracking by online learning of non-linear motion patterns and robust appearance models , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Andrew Gilbert,et al.  Tracking Objects Across Cameras by Incrementally Learning Inter-camera Colour Calibration and Patterns of Activity , 2006, ECCV.

[7]  Hossein Ragheb,et al.  MuHAVi: A Multicamera Human Action Video Dataset for the Evaluation of Action Recognition Methods , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[8]  Shaogang Gong,et al.  Multi-camera activity correlation analysis , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Matej Kristan,et al.  Dana36: A Multi-camera Image Dataset for Object Identification in Surveillance Scenarios , 2012, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.

[10]  Rita Cucchiara,et al.  3DPeS: 3D people dataset for surveillance and forensics , 2011, J-HGBU '11.

[11]  Tieniu Tan,et al.  Direction-based stochastic matching for pedestrian recognition in non-overlapping cameras , 2011, 2011 18th IEEE International Conference on Image Processing.

[12]  Yi-Ping Hung,et al.  An adaptive learning method for target tracking across multiple cameras , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Amit K. Roy-Chowdhury,et al.  Robust Tracking in A Camera Network: A Multi-Objective Optimization Framework , 2008, IEEE Journal of Selected Topics in Signal Processing.