Object detection is important and challenging task in computer vision applications such as surveillance, vehicle navigation, and human tracking. Video surveillance is a key technology to fight against terrorism and public safety management. In video surveillance, detection of moving objects from a video is important for object detection and behaviour understanding. Detection of moving objects in video streams is important process of revelation and background subtraction is popular approach for foreground segmentation. In this paper high speed background subtraction algorithm for moving object detection is proposed. The video is first converted to streams and then applied to convolution filter which removes high frequency noise components to obtain smoothened images. The smoothened images are then applied to background subtraction algorithm with adaptive threshold which gives detected object present in background image. The detected object is then applied to convolution filter to remove the spurious distorted pixels which improves the quality of image. The proposed architecture was designed using VHDL language and implemented using Spartan-6 (XC6SLX45-2csg324) FPGA kit. It is observed that the proposed technique is better compared to existing method in terms of image quality and speed of operations.

There is a need for innovation to improve the engagement and accessibility of rehabilitation programs for children and adults with upper extremity motor impairments due to neurodevelopmental disorders, acquired brain injuries, or spinal cord injuries. For this purpose, a computer game-based telerehabilitation platform (GTP) was developed to address this need. Through the application of a miniature inertial-based computer mouse and the wide variety of commercial computer games, the developed GTP can provide engaging task-specific exercises for the rehabilitation of manual dexterity (object handling and manipulation). A purpose-built repetitive task practice software (RTP) was also developed to gather event data and synchronize it with patient movements during gameplays. This provides automated monitoring and quantification of patients’ motor skills, while they practice a range of game-based exercises with their hand and/or arm. The GTP would initially be used in a supervised clinical setting followed by a transition to function at home and be monitored by clinician specialists. Clinical support for home and rural communities, with protocols that can be easily updated, will help increase accessibility to targeted and personalized solutions for patients and achieve the desired training effect.

Fast Fourier Transform (FFT) is widely used in image and video processing applications to convert the respective image or video frames into transform domain which is very helpful to extract the accurate features of that image or video frame for various real time applications. In this paper, efficient non-separable 8-point FFT architecture (DIT-FFT) is proposed which is implemented on Spartan-6 (xc6slx45-3csg324) FPGA. The proposed architecture consists of Data Format Conversion, Addition, Subtraction, Multiplier Equivalent and D-FF blocks respectively. The non-separable equations of 8-point DIT-FFT are derived from the respective Butterfly Diagram which is then implemented using basic logic gates which optimizes the hardware utilizations with the help of Complex Conjugate property. The constant multiplications present in the non-separable DIT-FFT equations are implemented through Adders and Shifters presents in Multiplier Equivalent block which further optimizes the overall hardware utilizations. Moreover the Q-format are used to increase the data accuracy of the architecture. The comparison results shows that the proposed architecture is better than existing in different prospectives.