Real-time human action recognition on an embedded, reconfigurable video processing architecture

Meng, Hongying; Freeman, Michael; Pears, Nick; Bailey, Chris

doi:10.1007/s11554-008-0073-1

Cited by 40 publications

(17 citation statements)

References 18 publications

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“…Wang et al [39], combine two algorithms to ensure object detection: Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). The proposed algorithm attempts to remove partial occlusions.…”

Section: Basic Concepts: Object Detection and Trackingmentioning

confidence: 99%

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

Ayed¹,

SabeurElkosantini²,

Abid³

2017

ijacsa

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Abstract-The video surveillance, such as an example of security system presents one of the powerful techniques used in advanced systems. Manual vision which is used to analyze video in the traditional approach should be avoided. An automated surveillance system based on suspicious behavior presents a great challenge to developers. The detection is encountered by complexity and time-consuming process. An abnormal behavior could be identified by different ways: actions, face, trajectory, etc. The characteristics of an abnormal behavior still presents a great problem. This paper proposes a specific System On Chip architecture for surveillance system based on Multi-Processor (MPSOC) and hardware accelerator. The aim is to accelerate the processing and obtain a reliable and accelerated suspicious behavior recognition. Finally, the experiment section proves the opportunity of the proposed system in terms of performance and cost.

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Section: Basic Concepts: Object Detection and Trackingmentioning

confidence: 99%

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

Ayed¹,

SabeurElkosantini²,

Abid³

2017

ijacsa

View full text Add to dashboard Cite

show abstract

“…In [25] a 600-fps real time action recognition system was proposed for four types of hand gestures. Meng and Freeman implemented a re-configurable system for action recognition and have tested the algorithm using a full human body action benchmark [26]. However, the 63% mean-average recognition rate on the KTH dataset [18] is well below state-of-the-art results.…”

Section: Introductionmentioning

confidence: 99%

Optimizing hardware design for Human Action Recognition

Borbon

Najjar

et al. 2016

2016 26th International Conference on Field Programmable Logic and Applications (FPL)

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Abstract-Human action recognition (HAR) is an important topic in computer vision having a wide range of applications: health care, assisted living, surveillance, security, gaming, etc. Despite significant amount of work having been conducted in this area in recent years, the execution speed still limits real-time applications. Moreover, it is highly desirable to have the computeintensive feature extraction stage done right at the output of the camera to extract and transfer only action feature in multicamera network setting and hence reduce network bandwidth requirement. In this work, we first evaluate the possibility to perform feature extraction under reduced precision fixed-point arithmetic to ease hardware resource requirements. We compared the Histogram of Oriented Gradient in 3D (HOG3D) feature extraction with state-of-the-art Convolutional Neural Networks (CNNs) methods and shown the later to be 75X slower than the former. Our experiment shows that by re-training the classifier with reduced data precision, the classification performs as well as the original double-precision floating-point. Based on this result, we implement an FPGA-based HAR feature extraction for near camera processing using fixed-point data representation and arithmetic. This implementation, using a single Xilinx Virtex 6 FPGA, achieves about 70x speedup over multicore CPU. Furthermore, a GPU implementation of HAR is introduced with 80x speedup over CPU (on an Nvidia Tesla K20). Last but not least, a power comparison is presented for the three platforms.

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“…Evolution of computer vision algorithms as well as Ambient intelligence (AmI) [6], [47], oriented towards ubiquitous computing and smart environments that react in an adaptive and active way to the presence and actions of objects implemented on embedded systems, has become an interesting area of research over the past decade. Such systems allow the implementation of early vision processes similar to the first neural layer in the retina, for pre-filtering conspicuous information [7].…”

Section: Introductionmentioning

confidence: 99%

Human behavioural analysis with self-organizing map for ambient assisted living

Appiah

Hunter

Lotfi

et al. 2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints.

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Real-time human action recognition on an embedded, reconfigurable video processing architecture

Cited by 40 publications

References 18 publications

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

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

Optimizing hardware design for Human Action Recognition

Human behavioural analysis with self-organizing map for ambient assisted living

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