6533b7d4fe1ef96bd12629e9

RESEARCH PRODUCT

Online Multi-Person Tracking by Tracker Hierarchy

Stan SclaroffJianming ZhangLiliana Lo Presti

subject

Computer scienceBitTorrent trackerbusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONInitializationTracking systemTracking (particle physics)Object detectionActive appearance modelVideo trackingTracking Experts DetectorComputer visionArtificial intelligenceMean-shiftbusiness

description

Tracking-by-detection is a widely used paradigm for multi-person tracking but is affected by variations in crowd density, obstacles in the scene, varying illumination, human pose variation, scale changes, etc. We propose an improved tracking-by-detection framework for multi-person tracking where the appearance model is formulated as a template ensemble updated online given detections provided by a pedestrian detector. We employ a hierarchy of trackers to select the most effective tracking strategy and an algorithm to adapt the conditions for trackers' initialization and termination. Our formulation is online and does not require calibration information. In experiments with four pedestrian tracking benchmark datasets, our formulation attains accuracy that is comparable to, or better than, the state-of-the-art pedestrian trackers that must exploit calibration information and operate offline.

http://hdl.handle.net/10447/97699