Abstract: Visual object tracking is an important task in computer vision and has a long list of applications such as video surveillance, robotics, intelligent vehicles, human-machine interaction, etc. In this proposal, we study the tracking problem from two aspects: algorithms and benchmarks. In the algorithm part, we first contribute a novel parallel tracking and verifying PTAV framework. The key idea of PTAV is to decompose tracking task into two components, a fast tracker and a robust verifier, which are implemented on two separate threads in an asynchronous fashion. With a carefully designed collaboration mechanism, PTAV enjoys both the high efficiency provided by tracker and the strong discriminative power by verifier.
Interlacing Self-Localization, Moving Object Tracking and Mapping for 3D Range Sensors
TLD Vision s.r.o.
Emily Crowe. School of Psychological Science. Abstract Tracking multiple objects as they move around the environment is a crucial everyday skill. This thesis investigated whether attention can be split unequally between moving objects to determine the nature of the attentional resource that underlies tracking. Fixed architectural models argue that a limited number of slots support tracking whereas flexible models propose a continuous pool of resources. A modified MOT task was developed which required participants to split their attention unequally between moving objects. Under both theories, unequal attention splitting is theoretically possible.
Post-doctoral Research. PhD Thesis. Book Chapters. Emilia Balas, Roy, S.
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