COROLA: A Sequential Solution to Moving Object Detection Using Low-rank Approximation
This work addresses real-time and long-duration video analysis needs in applications like surveillance and traffic monitoring, offering an incremental improvement over prior batch-based low-rank methods.
The paper tackles the problem of moving object detection in video sequences by proposing COROLA, an online sequential framework that uses low-rank approximation to model background and detect foreground objects, achieving superior accuracy and execution time compared to existing batch methods.
Extracting moving objects from a video sequence and estimating the background of each individual image are fundamental issues in many practical applications such as visual surveillance, intelligent vehicle navigation, and traffic monitoring. Recently, some methods have been proposed to detect moving objects in a video via low-rank approximation and sparse outliers where the background is modeled with the computed low-rank component of the video and the foreground objects are detected as the sparse outliers in the low-rank approximation. All of these existing methods work in a batch manner, preventing them from being applied in real time and long duration tasks. In this paper, we present an online sequential framework, namely contiguous outliers representation via online low-rank approximation (COROLA), to detect moving objects and learn the background model at the same time. We also show that our model can detect moving objects with a moving camera. Our experimental evaluation uses simulated data and real public datasets and demonstrates the superior performance of COROLA in terms of both accuracy and execution time.