Moving Object Classification Using a Combination of Static Appearance Features and Spatial and Temporal Entropy Values of Optical Flows

This paper proposes a new approach for classifying four types of moving objects in an intelligent transportation system. Pedestrians, cars, motorcycles, and bicycles are classified based on their side views from a fixed camera. A moving object is segmented and tracked using background subtraction, silhouette projection, an area ratio, a Kalman filter, and appearance correlation operations. For the classification of a segmented object, a combination of static and spatiotemporal features based on the cooccurrence of its appearance and the movements of its local parts is proposed. To extract the static appearance features, adaptive block-based gradient intensities and histograms of oriented gradients are proposed. For the spatiotemporal features, the optical-flow-based entropy values of instantaneous and short-term movements are proposed. The former finds the spatial entropy values of the orientations and the amplitudes of optical flows in a block to extract the local movement information from two consecutive image frames. The latter finds the temporal entropy values of the tracked optical flows in different orientation bins to extract the short-term movement information from several consecutive frames. Linear support vector machines with batch incremental learning are proposed to classify the four classes of objects. Experimental results from 12 test video sequences and comparisons with several feature descriptors show the effect of the proposed classification system and the advantage of the proposed features in classification.