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Evaluation of Low-Level Features for Real-World Surveillance Event Detection

Event detection targets at recognizing and localizing specified spatio-temporal patterns in videos. Most research of human activity recognition in the past decades experimented on relatively clean scenes with limited actors performing explicit actions. Recently, more efforts have been paid to the real-world surveillance videos in which the human activity recognition is more challenging due to large variations caused by factors, such as scaling, resolution, viewpoint, cluttered background, and crowdedness. In this paper, we systematically evaluate seven different types of low-level spatio-temporal features in the context of surveillance event detection (SED) using a uniform experimental setup. Fisher vector is employed to aggregate low-level features as the representation of each video clip. A set of random forests is then learned as the classification models. To bridge the research efforts and real-world applications, we utilize the NIST TRECVID SED as our testbed in which seven events are predefined involving different levels of human activity analysis. Strengths and limitations for each low-level feature type are analyzed and discussed.