In this paper we introduce the Embedded Vision Engine (EVE), a novel vision accelerator designed to complement digital signal processors on low- and mid-level vision algorithms. We illustrate EVE's performance on three important mid-level vision functions, the Hough transform for circles, integral image and calculating the Rotation invariant Binary Robust Independent Elementary Feature (RBRIEF) descriptor. EVE can execute these functions 4 times faster than the state-of-the-art digital signal processor. With other vision functions accelerated 3–12X, the acceleration of these popular vision functions contribute to the application level speed up of 5x on common automotive vision applications. This demonstrates that EVE, while working at powers comparable to the power of a DSP core, can deliver significant boost in performance.

Traditional video scene analysis depends on accurate background modeling to identify salient foreground objects. However, in many important surveillance applications, saliency is defined by the appearance of a new non-ephemeral object that is between the foreground and background. This midground realm is defined by a temporal window following the object's appearance; but it also depends on adaptive background modeling to allow detection with scene variations (e.g., occlusion, small illumination changes). The human visual system is ill-suited for midground detection. For example, when surveying a busy airline terminal, it is difficult (but important) to detect an unattended bag which appears in the scene. This paper introduces a midground detection technique which emphasizes computational and storage efficiency. The approach uses a new adaptive, pixel-level modeling technique derived from existing backgrounding methods. Experimental results demonstrate that this technique can accurately and efficiently identify midground objects in real-world scenes, including PETS2006 and AVSS2007 challenge datasets.

Automated video surveillance applications require accurate separation of foreground and background image content. Cost sensitive embedded platforms place realtime performance and efficiency demands on techniques to accomplish this task. In this paper we evaluate pixel-level foreground extraction techniques for a low cost integrated surveillance system. We introduce a new adaptive technique, multimodal mean (MM), which balances accuracy, performance, and efficiency to meet embedded system requirements. Our evaluation compares several pixel-level foreground extraction techniques in terms of their computation and storage requirements, and functional accuracy for three representative video sequences. The proposed MM algorithm delivers comparable accuracy of the best alternative (Mixture of Gaussians) with a 6X improvement in execution time and an 18% reduction in required storage.

The paper proposes several techniques for optimizing the JPEG2000 binary arithmetic encoder on very long instruction word (VLIW) architectures. Binary arithmetic coding (BAC) contains a large amount of conditional and sequential processing steps that make parallelism on VLIW devices difficult to realize. The paper illustrates an optimized software implementation that can software pipeline on a VLIW device. The Texas Instruments (TI) TMS320C64x digital signal processor (DSP) was chosen as the implementation platform. Results of our optimized code show a 2.4/spl times/ performance speed-up over a straightforward implementation of the arithmetic encoder as defined in the JPEG2000 standard.