Real-time object detection system on PAC Duo platform

Cho, Chao-Yi; Yang, Jen-Kuei; Tseng, Shau-Yin; Lee, Ivan; Sheu, Ming‐Hwa

doi:10.1109/icsps.2010.5555505

Cited by 3 publications

(2 citation statements)

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“…Nowadays low-cost embedded surveillance systems (Valentine et al 2010) are developed for real-time applications, but they are hindered by memory space and processor speed. Therefore, many light-weight methods (Chiu, Ku, and Liang 2010;Cho et al 2010;Valentine et al 2010;Wang, Velipasalar, and Casares 2010;Barnich and Van Droogenbroeck 2011;Tsai, Sheu, and Lin 2012) have been proposed in order to reduce memory consumption and processing time. A common method for moving object detection is background subtraction.…”

Section: Introductionmentioning

confidence: 99%

Fast image foreground object segmentation based on block texture for embedded system implementation

Yang

Sheu

Tsai

2014

Journal of the Chinese Institute of Engineers

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In this paper, a novel block-based foreground object detection method based on block texture is presented. It can significantly reduce the memory usage when constructing the background model in dynamic scenes. The proposed background model and detection algorithm are suitable for implementing on embedded system platforms with resource limitations. The experimental results of processing benchmark videos show that our method has outcomes that are very close to ground truth segmentation. In addition, the proposed method requires approximately 23.97% less memory than the latest algorithms. Finally, the proposed approach is implemented on an embedded system platform. The processing speed can achieve a real-time rate of at least 20 fps, which is an improvement of 17.64% as compared to the latest algorithms.

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Section: Introductionmentioning

confidence: 99%

Fast image foreground object segmentation based on block texture for embedded system implementation

Yang

Sheu

Tsai

2014

Journal of the Chinese Institute of Engineers

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“…The approach has low memory consumption and computational complexity, but it could not overcome a sudden scene change. Due to the mass calculations and I/O processing required on multi-media and communication systems, [10] proposes a high efficient but low-power consumption embedding platform, PAC Duo Soc. It is able to process object detection in 640×480 resolution for 30 frames per second.…”

Section: Introductionmentioning

confidence: 99%

Region-Based Background Subtraction for Complex Sense on Embedded Platforms

Tsai

Sheu

Lin

2012

2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing

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This paper presents a background modeling method to detect foreground objects precisely. An object detection system must face the problem of moving background, illumination changes, chaotic etc. in real applications. The proposed background model can be established and updated efficiently from real-time image sequences to overcome the various illumination environments. Based on our approach, the static background model and adaptive multi-layer dynamic background model are constructed completely and correctly. In order to extract the moving object precisely, the proposed detection method considers the dependence between adjacent pixels by using homogeneous region analysis. Experimental results indicate that the proposed approach has lower error rate than other existing methods. Furthermore, our approach has been implemented in TI TMS320DM6446 Davinci development platform, and it can achieve 18 frames per second for benchmark images of size 768×576.I.

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