Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 compute-intensive feature extraction stage done right at the output of the camera to extract and transfer only action feature in multi-camera network setting and hence reduce network bandwidth requirement. In this work,  first evaluate the possibility to perform feature extraction under reduced precision fixed-point arithmetic to ease hardware resource requirements.  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 75× slor than the former. Our experiment shows that by re-training the classifier with reduced data precision, the classification performs as ll as the original double-precision floating-point. Based on this result,  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 70× speedup over multicore CPU. Furthermore, a GPU implementation of HAR is introduced with 80× speedup over CPU (on an Nvidia Tesla K20). Last but not least, a por comparison is presented for the three platforms.
['Xiaoyin Ma', 'Jose Rodriguez Borbon', 'Walid A. Najjar', 'Amit K. Roy-Chowdhury']
I am writing a research paper on a idea Optimizing hardware design for Human Action Recognition
[{"from": "human", "value": "I am writing a research paper on a idea Optimizing hardware design for Human Action Recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 compute-intensive feature extraction stage done right at the output of the camera to extract and transfer only action feature in multi-camera network setting and hence reduce network bandwidth requirement. In this work,  first evaluate the possibility to perform feature extraction under reduced precision fixed-point arithmetic to ease hardware resource requirements.  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 75\u00d7 slor than the former. Our experiment shows that by re-training the classifier with reduced data precision, the classification performs as ll as the original double-precision floating-point. Based on this result,  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 70\u00d7 speedup over multicore CPU. Furthermore, a GPU implementation of HAR is introduced with 80\u00d7 speedup over CPU (on an Nvidia Tesla K20). Last but not least, a por comparison is presented for the three platforms."}]