Fusion Strategies for Context Based Object Detection in Video Sequences

摘要

A highly challenging object detection task is the recognition of relevant events in outdoor applications, such as it is the case in sport broadcasts. Changing illumination, different weather conditions, and noise in the imaging process are the most important issues that require a truly robust detection system. The original contribution of this work is to take advantage of a dynamic integration of object beliefs from different evidences of spatial and temporal context to receive a recursively updated object hypothesis, with the aim to render object detection more robust. The object representation is outlined in a probabilistic framework to enable reasoning on multiple instances of detection results and decision making based on statistical evaluations. The representation is based on the local appearances of the objects, and therefore makes the interpretation more robust to occlusion by enabling reasoning based on spatial context between the appearances of individual object parts. Reasoning is driven by the evaluation of Bayesian decision fusion of the single probabilistic local image interpretations. The detection system is evaluated on the detection of company logos in extensive video material from Formula One broadcasts. The experimental results demonstrate that fusion is crucial to improve robustness and accuracy of the outdoor detection system.