The CPHD and R-RANSAC trackers applied to the VIVID dataset
Ramona Georgescu, Peter C. Niedfeldt, Shuo Zhang, Amit Surana, Alberto Speranzon, Ozgur Erdinc · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
In this work, two multitarget trackers - the Cardinalized Probability Hypothesis Density (CPHD) filter and the Recursive Random Sample Consensus (R-RANSAC) algorithm - were applied to three scenarios of the Video Verification of IDentity (VIVID) dataset provided by DARPA. The dataset consists of real video data of multiple cars observed from an unmanned aerial vehicle (UAV) and includes challenging situations such as dense traffic and occlusions. The same detector output was given to each tracker and the same metrics of performance were computed in order to ensure fair comparison of the two tracking approaches. The results show the CPHD did better overall, which was to be expected given that it is the more mature approach.