Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting ILP, TLP, and DLP with the polymorphous TRIPS architecture
Proceedings of the 30th annual international symposium on Computer architecture
Detection and Location of People in Video Images Using Adaptive Fusion of Color and Edge Information
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
ROS-DMA: A DMA Double Buffering Method for Embedded Image Processing with Resource Optimized Slicing
RTAS '06 Proceedings of the 12th IEEE Real-Time and Embedded Technology and Applications Symposium
The cell broadband engine: exploiting multiple levels of parallelism in a chip multiprocessor
International Journal of Parallel Programming
Cat-tail dma: efficient image data transport for multicore embedded mobile systems
Journal of Mobile Multimedia
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Current trends in microprocessor design integrate several autonomous processing cores onto the same die. These multicore architectures are particularly well-suited for computer vision applications, where it is typical to perform the same set of operations repeatedly over large datasets. These memory- and computation-intensive applications can reap tremendous performance and accuracy benefits from concurrent execution on multi-core processors. However, cost-sensitive embedded platforms place real-time performance and efficiency demands on techniques to accomplish this task. Furthermore, parallelization and partitioning techniques that allow the application to fully leverage the processing capabilities of each computing core are required for multi-core embedded vision systems. In this paper, we evaluate background modeling techniques on a multicore embedded platform, since this process dominates the execution and storage costs of common video analysis workloads. We introduce a new adaptive backgrounding technique, multimodal mean, which balances accuracy, performance, and efficiency to meet embedded system requirements. Our evaluation compares several pixel-level background modeling techniques in terms of their computation and storage requirements, and functional accuracy for three representative video sequences, across a range of processing and parallelization configurations. We show that the multimodal mean algorithm delivers comparable accuracy of the best alternative (Mixture of Gaussians) with a 3.4脳 improvement in execution time and a 50% reduction in required storage for optimal block processing on each core. In our analysis of several processing and parallelization configurations, we show how this algorithm can be optimized for embedded multicore performance, resulting in a 25% performance improvement over the baseline processing method.