Resource-Efficient Salient Foreground Detection for Embedded Smart Cameras br Tracking Feedback

Abstract

Battery-powered wireless embedded smart cameras have limited processing power, memory and energy. Since video processing tasks consume significant amount of power, the problem of limited resources becomes even more pronounced, and necessitates designing light-weight algorithms suitable for embedded platforms. In this paper, we present a resource-efficient salient foreground detection and tracking algorithm. Contrary to traditional methods that implement foreground object detection and tracking independently and in a sequential manner, the proposed method uses the feedback from the tracking stage in the foreground object detection. We compare the proposed method with a sequential method on the microprocessor of an embedded smart camera, and present the savings in the processing time and energy consumption and the gain in the lifetime of a battery-powered camera for different scenarios. The presented method provides significant savings in terms of the processing time of a frame. We take advantage of these savings by sending the microprocessor to idle state at the end of processing a frame, and when the scene is empty.

Energy-efficient foreground object detection on embedded smart cameras by hardware-level operations

“…The reasons include the better use of the memory controller and the memory resources, a reduction in the amount of data that is moved from the image sensor to the main memory at each frame, and not occupying the main microprocessor with this task. One application to take advantage of cropping is the localized foreground object detection and tracking algorithm that is introduced in [12]. This application will be discussed in more detail in Section 4.…”

“…This will henceforth be referred to as the sequential method. Casares et al [12] introduced the feedback method that is a lightweight foreground object detection and tracking algorithm suitable for embedded platforms. In this method, feedback from the tracking stage is used to determine search regions, and perform detection and tracking in those regions instead of the whole frame.…”

“…As mentioned in Section 4, a lightweight object detection and tracking method is presented in [12], which employs feedback from the tracking stage to determine search regions, and perform detection and tracking in those regions only instead of the whole frame. This method provides significant savings in processing time, and thus allows us to increase idle state durations of cameras to increase the battery-life.…”

“…Casares et al [12] introduced an algorithm for resourceefficient foreground object detection, and presented the savings in processing time and energy consumption on CITRIC cameras. They obtained the savings in software-level.…”

An Automated Surveillance System based on Multi-Processor System-on-Chip and Hardware Accelerator

“…Wang et al, [37] propose Incremental Multiple Principle Component Analysis (MPCA) algorithm for detection and tracking. Based on the time sequence, this method manages the variation of image's streams.…”