A single-chip 600-fps real-time action recognition system employing a hardware friendly algorithm

Hou, Zhongjun; Zhu, Hongbo; Zheng, Nanning; Shibata, Tadashi

doi:10.1109/iscas.2014.6865247

Cited by 4 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hou et al [29] proposes an FPGA real-time HAR system operating at 600 fps. It has a recognition rate of 93.2% when working with a human gesture database with four actions.…”

Section: Related Workmentioning

confidence: 99%

“…The acceleration of HOG3D has not received the same attention as that of HOG2D. The work in [29] targets HAR applications in FPGAs although it operates at 600 fps while using images size 320×240. This design has a recognition rate of 93.2% working with a small set of actions.…”

Section: F Comparison With Other Workmentioning

confidence: 99%

“…In comparison, our work achieves a throughput of 1,311 fps on 640 × 480 images when eight videos streams are processed in parallel. Also, while the work in [67] and [29] target datasets having few classes, our work targets datasets having over 50 classes.…”

Section: F Comparison With Other Workmentioning

confidence: 99%

See 2 more Smart Citations

Heterogeneous Acceleration of HAR Applications

Rodriguez-Borbon

Roy-Chowdhury

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

Human action recognition (HAR) is an important field of research that intercepts with areas such as image processing, computer vision, and the design of fast algorithms, among others. HAR has several important applications including healthcare monitoring, security and surveillance, assisted living, smart homes, and video search and indexing. Despite recent developments in the field, major challenges remain. For instance, HAR is computationally expensive. Tasks such as video preprocessing, feature extraction, feature quantization, and feature classification require the execution of millions of arithmetic operations for a video sequence lasting a few seconds. To address these problems, we propose a heterogeneous approach that is based on an extensive algorithmic and experimental analysis of the histogram of gradients (HOG3D) application. We divide the application into four stages and evaluate each on CPU, GPU, and FPGA platforms. Our heterogeneous design combines the strengths of both FPGA and GPU platforms, and achieves a 1.3X speedup compared with a state-of-the-art GPU while being 1.5X more energy efficient than other homogeneous solutions, including FPGA-based designs. Moreover, our heterogeneous HAR design using fixed-point arithmetic has comparable accuracy to those of HAR algorithms using single precision floating point arithmetic.

show abstract

“…Hou et al [29] proposes an FPGA real-time HAR system operating at 600 fps. It has a recognition rate of 93.2% when working with a human gesture database with four actions.…”

Section: Related Workmentioning

confidence: 99%

Section: F Comparison With Other Workmentioning

confidence: 99%

See 1 more Smart Citation

Heterogeneous Acceleration of HAR Applications

Rodriguez-Borbon

Roy-Chowdhury

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

show abstract

“…The design of a vision processing chip that can be used for gesture recognition is described in [24]. 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].…”

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)

View full text Add to dashboard Cite

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.

show abstract

A 203 FPS VLSI Architecture of Improved Dense Trajectories for Real-Time Human Action Recognition

Lin

Chen

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

A single-chip 600-fps real-time action recognition system employing a hardware friendly algorithm

Cited by 4 publications

References 12 publications

Heterogeneous Acceleration of HAR Applications

Heterogeneous Acceleration of HAR Applications

Optimizing hardware design for Human Action Recognition

A 203 FPS VLSI Architecture of Improved Dense Trajectories for Real-Time Human Action Recognition

Contact Info

Product

Resources

About