Occlusion activity detection algorithm using Kalman filter for detecting occluded multiple objects

This paper proposes the detection method of occluded moving objects using occlusion activity detection algorithm. When multiple objects are occluded between them, a simultaneous feature based tracking of multiple objects using tracking filters fails. To estimate feature vectors such as location, color, velocity, and acceleration of a target are critical factors that affect the tracking performance and reliability. To resolve this problem, the occlusion activity detection algorithm is addressed. Occlusion activity detection method provides the occlusion status of next state using the Kalman prediction equation. By using this predicted information, the occlusion status is verified once again in its current state. If the occlusion status is enabled, an object association technique using a partial probability model is applied. For an experimental evaluation, the image sequences for a scenario in which three rectangles are moving within the image frames are made and evaluated. Finally, the proposed algorithms are applied to real image sequences. Experimental results in a natural environment demonstrate the usefulness of the proposed method.