Multimodal 3D histogram for moving object detection

Moving object detection is a popular research area due to its vast application in computer vision. Based on application areas, a number of moving object detection methods have been discovered. This work proposes a real-time method based on 3D histogram and temporal multiple mode selection, suited towards a vast majority of dynamic and noisy backgrounds, congested backgrounds and slow foregrounds. It is a generalization of [1] demonstrating improved performance. The temporal distribution of a video sequence can provide a history of the foreground motion and relatively static background. However, a dynamic background is identified by multiple intensity levels in the temporal distribution, and hence, cannot be properly modeled by a single histogram mode. The proposed multiple mode selection process involves identifying the dominant modes of the temporal distribution and assigning them to a multimodal background. The work provides a detailed analysis of the proposition, a clear description of the proposed algorithm and an extensive number of tests with some of the well-known methods in literature. It also demonstrates the improvement over its predecessor in several types of scenarios covered by a large number of datasets. Both qualitative and quantitative comparisons are carried out to demonstrate the applicability and accuracy of the proposed method.