ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
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
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The paper presents a probabilistic tracking framework to fuse high-level object detection cues with low-level image feature cues using particle filters. First, an adaptive ICONDENSATION (AICONDENSATION) is introduced to better exploit object detection cues to guide importance sampling, where the proposal distribution is derived in a more principled approach using data association methods so that mixture weights can be adapted dynamically rather than fixed in ICONDENSATION. An adaptive detection fusion CONDENSATION (AFCONDENSATION) is further presented to directly fuse high-level object detection cues with low-level cues, where mixture weights are also adapted and it is shown that weight correction in ICONDENSATION actually is not necessary. Results on sequences with both simulated and real detections show improved performance of AI/AFCONDENSATION in comparison with ICONDENSATION.