Adaptive visual tracking system using artificial intelligence

Abstract

The video sequences provide more information than the still images about how objects and scenarios change over time. However, video needs more space for storage and wider bandwidth for transmission.Hence, more challenges are encountered in retrieval and event detection in large data sets during the visual tracking. In the proposed method, the object planes are segmented properly and the motion parameters are derived for each plane to achieve a better compression ratio. Most of the existing tracking algorithms in dynamic scenes consider the target alone and the background information are often ignored.Therefore, they are failed to track the target. In order to optimize the existing system, a robust visual tracking algorithm is to be developed which will adapt the drastic changes of target appearance without background influence. The initial occlusion of non target objects in the background can effectively be addressed by the integration of multiple cues and spatial information in target representation. With the combination of motion information and detection methods, the target can be reacquired when complete occlusion of target occurs.

Moving Object Detection for Video Surveillance

“…Since most of these transform coefficients have small magnitudes, they can be entirely discarded with an acceptable error. The error between the original and compressed video frames is usually enumerated by the factors, namely, mean square error (MSE) and peak signal to noise ratio (PSNR) [ 2 ]. The MSE between two frames “ f ” and “ s ” is given by the following equation: where i and j denote the sum of all pixels in the image frames and N is the number of pixels per frame.…”

“…Object detection is the first and foremost step as it is directly influenced by the background information. Since there is considerable irrelevant and redundant information in the video across space and time, the video data need to be compressed at the earliest in video surveillance applications [ 2 ]. Compression can be achieved by minimizing the spatial and temporal redundancies present in the video.…”

Object detection and multi‐object tracking based on optimized deep convolutional neural network and unscented Kalman filtering

“…The video having a longer duration poses many frames when compared to videos having less duration. The frames' total number is represented in Equation (1).…”

“…Due to superfluous data in video across time and space, video data should be compressed in video applications. 1 Compression can be attained by reducing spatial and temporal inconsistencies contained in the videos. 2,3 In previous days, the video is compressed by minimizing frame size or by skipping frames with small video quality degradation.…”

Moving vehicle detection from video sequences for Traffic Surveillance System

“…The image is in white or block depends on pixel value. There are countless procedures and techniques for threshold value selection [25]. Particularly histogram is a promising method.…”