Abstract
Multi-target tracking in video helps in gathering information from motion patterns to describe behaviors (e.g., sport team formations), to detect events of interest (e.g., crossing streets in forbidden locations) and to facilitate content retrieval (e.g., automatic highlights generation). Several challenges affect multi-target tracking, including color and shape similarities, occlusions and abrupt motion variations. We define a generic flow diagram that we use to discuss and compare the main stages of multi-target trackers, namely feature extraction, target prediction, localization or association, and post-processing. Trackers may also learn about the environment they operate in (contextual information) and update the target model they use in order to enhance the localization task. We finally summarize the properties of the surveyed multi-target trackers and introduce open research problems in video tracking research.
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