Pedestrian activity classification using patterns of motion and histogram of oriented gradient

This paper presents a view-based spatio-temporal template formation scheme for pedestrian activity recognition and classification. It emphasizes on detailed classification of the entire wide range of pedestrian actions in the context of road crossing and their recognition from a viewpoint of a static car in the traffic signal. For this purpose, we develop a new pedestrian action dataset containing 160 videos of eight actions with twenty subjects per action. First, action sequences are converted into Motion History Image where silhouette sequence is condensed into gray scale images, while dominant motion information is preserved. Then, Histogram of Oriented Gradient of the Motion History Image is calculated from local appearance and shape within the image for feature extraction. Finally, Support Vector Machine is employed for pedestrian action classification. The demonstrated method successfully classified 91% of the pedestrian action. To compare the method with other methods, we also implement some established methods with the developed dataset.