Video segmentation for content based retrieval has traditionally been done using shot cut detection algorithms
that search for abrupt changes in scene content. Surveillance videos however, usually use still cameras, and do
not contain any shots. Hence, a novel high level semantic change detection algorithm is proposed in this paper
that uses object trajectory features to segment surveillance footage. These trajectory features are extracted
automatically, using background subtraction and a multiple blob tracking algorithm. The trajectory features
are first used to remove false object detections from background subtraction. Semantics extracted from the
remaining object trajectories are then used to segment the video. The results of the algorithm when applied to
surveillance data are compared with hand labeled segmentation to obtain precision recall curves and harmonic
mean. Comparisons with traditional background subtraction and video segmentation algorithms show a drastic
improvement in performance.
Segmentation of objects in a video sequence is a key stage in most content-based retrieval systems. By further analysing the behaviour of these objects, it is possible to extract semantic information suitable for higher level content analysis. Since interesting content in a video is usually provided by moving objects, motion is a key feature to be used for pre content analysis segmentation. A motion based segmentation algorithm is presented in this paper that is both efficient and robust. The algorithm is also robust to the type of camera motion. The framework presented consists of three stages. These are the motion estimation stage, foreground detection stage and the refinement stage. An iteration of the first two stages, adaptively altering the motion estimation parameters each time, results in a joint segmentation and motion estimation approach that is extremely fast and accurate. Two dimensional histograms are used as a tool to carry out the foreground detection. The last stage uses morphological approaches as well as a prediction of foreground regions in future frames to further refine the segmentation. In this paper, results obtained from simple and traditional approaches are compared with that of the proposed framework in the wildlife domain.