Performance characterization in computer vision: A guide to best practices
Computer Vision and Image Understanding
Knowledge Aquisition and Data Storage in Mobile GeoSensor Networks
GeoSensor Networks
Accurate appearance-based Bayesian tracking for maneuvering targets
Computer Vision and Image Understanding
Fast Object Tracking in Intelligent Surveillance System
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
Urban Vehicle Tracking Using a Combined 3D Model Detector and Classifier
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Discovering private trajectories using background information
Data & Knowledge Engineering
A reliable event-driven strategy for real-time multiple object tracking using static cameras
Advances in Multimedia
Resolving the ambiguity of real-time multiple object tracking using static cameras
International Journal of Intelligent Systems Technologies and Applications
Learning a quality-based ranking for feature point trajectories
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
On-line object tracking with semi-supervised boosting based particle filter
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Robust hand tracking in realtime using a single head-mounted RGB camera
HCI'13 Proceedings of the 15th international conference on Human-Computer Interaction: interaction modalities and techniques - Volume Part IV
Unsupervised Tracking, Roughness and Quantitative Indices
Fundamenta Informaticae - Cognitive Informatics and Computational Intelligence: Theory and Applications
| Hi-index | 0.01 |
This paper discusses methods behind tracker evaluation, the aim being to evaluate how well a tracker is able to determine the position of a target object. Few metrics exist for positional tracker evaluation; here the fundamental issues of trajectory comparison are addressed, and metrics are presented which allow the key features to be described. Often little evaluation on how precisely a target is tracked is presented in the literature, with results detailing for what percentage of the time the target was tracked. This issue is now emerging as a key aspect of tracker performance evaluation. The metrics developed are applied to real trajectories for positional tracker evaluation. Data obtained from a sports player tracker on video of a 5-a-side soccer game, and from a vehicle tracker, is analysed. These give quantitative positional evaluation of the performance of computer vision tracking systems, and provides a framework for comparison of different methods and systems on benchmark data sets.