Algorithmic and software techniques for embedded vision on programmable processors

Kisačanin, Branislav; Nikolic, Zoran

doi:10.1016/j.image.2010.02.003

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…However, a number of implementation techniques were introduced to overcome these issues [5][6][7]. Previous work using static cameras [8][9][10] describe the implementation of smart surveillance sensors, for example for object detection and tracking.…”

Section: Related Workmentioning

confidence: 99%

DSP Embedded Smart Surveillance Sensor with Robust SWAD-Based Tracker

Caterina

Hunter

Soraghan

2012

Advanced Concepts for Intelligent Vision Systems

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Abstract. Smart video analytics algorithms can be embedded within surveillance sensors for fast in-camera processing. This paper presents a DSP embedded video analytics system for object and people tracking, using a PTZ camera. The tracking algorithm is based on adaptive template matching and it employs a novel Sum of Weighted Absolute Differences. The video analytics is implemented on the DSP board DM6437 EVM and it automatically controls the PTZ camera, to keep the target central to the field of view. The EVM is connected to the network and the tracking algorithm can be remotely activated, so that the PTZ enhanced with the DSP embedded video analytics becomes a smart surveillance sensor. The system runs in real-time and simulation results demonstrate that the described SWAD outperforms other template matching measures in terms of efficiency and accuracy.

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Section: Related Workmentioning

confidence: 99%

DSP Embedded Smart Surveillance Sensor with Robust SWAD-Based Tracker

Caterina

Hunter

Soraghan

2012

Advanced Concepts for Intelligent Vision Systems

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“…As vision algorithms are applied in embedded systems, optimization of vision algorithms [4] and software [5] is critical to achieve high performance. Among the greatest challenges in achieving high code efficiency in an embedded environment is the reformulation of algorithms to exploit the advantages of the fixed-point data representation and direct memory access.…”

Section: Requirements For Embedded Visionmentioning

confidence: 99%

An optimized vision library approach for embedded systems

Dedeoǧlu

Kisačanin

Moore

et al. 2011

CVPR 2011 Workshops

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There is an ever-growing pressure to accelerate computer vision applications on embedded processors for wide-ranging equipment including mobile phones, network cameras, and automotive safety systems. Towards this goal, we propose a software library approach that eases common computational bottlenecks by optimizing over 60 low-and mid-level vision kernels. Optimized for a digital signal processor that is deployed in many embedded image & video processing systems, the library was designed for typical high-performance and low-power requirements. The algorithms are implemented in fixedpoint arithmetic and support block-wise partitioning of video frames so that a direct memory access engine can efficiently move data between on-chip and external memory. We highlight the benefits of this library for a baseline video security application, which segments moving foreground objects from a static background. Benchmarks show a ten-fold acceleration over a bit-exact yet unoptimized C language implementation, creating more computational headroom to embed other vision algorithms.

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“…Recently, Cernium Corporation has developed a standalone embedded vision system product called archerfish [15], which detects and reports various events of interest in surveillance video in real time. In [1], the scope of embedded vision systems, different hardware platforms, software implementation and various optimization techniques have been discussed in detail. Among the available embedded hardware platforms like Digital Signal Processor (DSP), Field Programmable Gate Array (FPGA) and Graphical Processing Unit (GPU), we have chosen DSP for our study for its advantageous development time, flexibility and performance.…”

Section: Introductionmentioning

confidence: 99%

“…Among the available embedded hardware platforms like Digital Signal Processor (DSP), Field Programmable Gate Array (FPGA) and Graphical Processing Unit (GPU), we have chosen DSP for our study for its advantageous development time, flexibility and performance. In the cases, where a large number of different algorithms are involved, each running for short time, the software programmable architectures have an advantage over hardware programmable approaches [1].…”

Section: Introductionmentioning

confidence: 99%

Embedded implementation of facial landmarks detection using extended active shape model approach

Karuppusamy

Jerome

Shankar

2014

2014 International Conference on Embedded Systems (ICES)

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Facial landmark detection is very crucial for different kinds of video analytics in robotic applications. However, resource limited characteristics of an embedded system pose many challenges to implement a standalone robotic vision systems like human machine interaction, driver drowsiness monitoring and surveillance applications. The main objective of this paper is to propose effective implementation strategies for the extended Active Shape Model (ASM) based facial landmark detection algorithm using programmable resource constraint digital video processor and evaluate it in real-time. In extended ASM approach, robust detection of face, eyes and mouth is very important to initialize the shape model which defined by facial landmarks. Using this initialized shape model, the landmarks of the given test face are obtained through minimizing the distance with learned shape model iteratively. For different face parts detection, we have used robust and fast Viola-Jones detector (V-J detector) and optimized it at various levels to meet the computation and memory constraints. The implemented embedded vision system has been evaluated using visible light camera in real-world scenarios and its robustness and time consumption has been reported in detail. Through different experiments, we show that the proposed implementation framework achieves more than 98% face parts detection rate and mean error of less than 2 pixels in facial landmarks accuracy.

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Algorithmic and software techniques for embedded vision on programmable processors

Cited by 6 publications

References 29 publications

DSP Embedded Smart Surveillance Sensor with Robust SWAD-Based Tracker

DSP Embedded Smart Surveillance Sensor with Robust SWAD-Based Tracker

An optimized vision library approach for embedded systems

Embedded implementation of facial landmarks detection using extended active shape model approach

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