Fibonacci heaps and their uses in improved network optimization algorithms
Journal of the ACM (JACM)
Automatic Performance Evaluation for Video Text Detection
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Evaluating Multi-Object Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Vehicle and Person Tracking in Aerial Videos
Multimodal Technologies for Perception of Humans
Real time architectures for moving-objects tracking
ARC'07 Proceedings of the 3rd international conference on Reconfigurable computing: architectures, tools and applications
Performance evaluation of text detection and tracking in video
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Evaluating the effects of MJPEG compression on motion tracking in metro railway surveillance
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
An evaluation framework for stereo-based driver assistance
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Real-time probabilistic data association over streams
Proceedings of the 7th ACM international conference on Distributed event-based systems
Hi-index | 0.00 |
The need for empirical evaluation metrics and algorithms is well acknowledged in the field of computer vision. The process leads to precise insights to understanding current technological capabilities and also helps in measuring progress. Hence designing good and meaningful performance measures is very critical. In this paper, we propose two comprehensive measures, one each for detection and tracking, for video domains where an object bounding approach to ground truthing can be followed. Thorough analysis explaining the behavior of the measures for different types of detection and tracking errors are discussed. Face detection and tracking is chosen as a prototype task where such an evaluation is relevant. Results on real data comparing existing algorithms are presented and the measures are shown to be effective in capturing the accuracy of the detection/tracking systems.