TRIC-track: Tracking by Regression with Incrementally Learned Cascades
2015Vol. 33, pp. 4337–4345
Citations Over TimeTop 10% of 2015 papers
Abstract
This paper proposes a novel approach to part-based tracking by replacing local matching of an appearance model by direct prediction of the displacement between local image patches and part locations. We propose to use cascaded regression with incremental learning to track generic objects without any prior knowledge of an object's structure or appearance. We exploit the spatial constraints between parts by implicitly learning the shape and deformation parameters of the object in an online fashion. We integrate a multiple temporal scale motion model to initialise our cascaded regression search close to the target and to allow it to cope with occlusions. Experimental results show that our tracker ranks first on the CVPR 2013 Benchmark.
Related Papers
- → AEG: Automatic Exploit Generation(2018)209 cited
- → Exploit Kits: The production line of the Cybercrime economy?(2015)33 cited
- → PExy: The Other Side of Exploit Kits(2014)24 cited
- → AEMB: An Automated Exploit Mitigation Bypassing Solution(2021)5 cited
- Evaluation of Two Host-Based Intrusion Prevention Systems(2005)