A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking and data association
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comprehensive Colour Image Normalization
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Combing IMM Filtering and MHT Data Association for Multitarget Tracking
SSST '97 Proceedings of the 29th Southeastern Symposium on System Theory (SSST '97)
Foreground Object Detection in Changing Background Based on Color Co-Occurrence Statistics
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Foreground object detection from videos containing complex background
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A Dynamic Conditional Random Field Model for Foreground and Shadow Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Fuzzy Sets and Systems
Robust Object Tracking by Hierarchical Association of Detection Responses
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Online, Real-time Tracking and Recognition of Human Actions
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
A Multiple Hypothesis Tracking Method with Fragmentation Handling
CRV '09 Proceedings of the 2009 Canadian Conference on Computer and Robot Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Multiple-Target Tracking by Spatiotemporal Monte Carlo Markov Chain Data Association
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking groups of people with a multi-model hypothesis tracker
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Mean Shift tracking with multiple reference color histograms
Computer Vision and Image Understanding
Foreground and shadow detection based on conditional random field
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Probabilistic data association methods in visual tracking of groups
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Continuous tracking within and across camera streams
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Extended Object Tracking Using Monte Carlo Methods
IEEE Transactions on Signal Processing
A Multiple-Hypothesis Approach for Multiobject Visual Tracking
IEEE Transactions on Image Processing
Hi-index | 12.05 |
Multiple object tracking is a fundamental subsystem of many higher level applications such as traffic monitoring, people counting, robotic vision and many more. This paper explains in details the methodology of building a robust hierarchical multiple hypothesis tracker for tracking multiple objects in the videos. The main novelties of our approach are anchor-based track initialization, prediction assistance for unconfirmed track and two virtual measurements for confirmed track. The system is built mainly to deal with the problems of merge, split, fragments and occlusion. The system is divided into two levels where the first level obtains the measurement input from foreground segmentation and clustered optical flow. Only K-best hypothesis and one-to-one association are considered. Two more virtual measurements are constructed to help track retention rate for the second level, which are based on predicted state and division of occluded foreground segments. Track based K-best hypothesis with multiple associations are considered for more comprehensive observation assignment. Histogram intersection testing is performed to limit the tracker bounding box expansion. Simulation results show that all our algorithms perform well in the surroundings mentioned above. Two performance metrics are used; multiple-object tracking accuracy (MOTA) and multiple-object tracking precision (MOTP). Our tracker have performed the best compared to the benchmark trackers in both performance evaluation metrics. The main weakness of our algorithms is the heavy processing requirement.