Estimation of Dynamic Background and Object Detection in Noisy Visual Surveillance

Abstract

Abstract-Dynamicbackground subtraction in noisy environment for detecting object is a challenging process in computer vision. The proposed algorithm has been used to identify moving objects from the sequence of video frames which contains dynamically changing backgrounds in the noisy atmosphere. There are many challenges in achieving a robust background subtraction algorithm in the external noisy environment. In connection with our previous work, in this paper, we have proposed a methodology to perform background subtraction from moving vehicles in traffic video sequences that combines statistical assumptions of moving objects using the previous frames in the dynamically varying noisy situation. Background image is frequently updated in order to achieve reliability of the motion detection. For that, a binary moving objects hypothesis mask is constructed to classify any group of lattices as being from a moving object based on the optimal threshold. Then, the new incoming information is integrated into the current background image using a Kalman filter. In order to improve the performance, a post-processing has been done. It has been accomplished by shadow and noise removal algorithms operating at the lattice which identifies object-level elements. The results of post-processing can be used to detect object more efficiently. Experimental results and analysis show the prominence of the proposed approach which has achieved an average of 94% accuracy in real-time acquired images.

Statistical Review of Various Object Detection Techniques

“…These variations amongst the current frame and the reference frame with respect to pixels signifies the existence of an objects that are moving [6]. At present, mean and median filters [7] are more extensively used in order to realize the background modeling. However, this provides the maximum complete information about the object if background is well-known.…”

A Survey of Moving Target Detection Using Dynamic Background in Video Surveillance System

“…The variations between current video frames to that of the reference frame in terms of pixels signify existence of moving objects [10]. Currently, mean filter and median filter [11] are widely used to realize background modeling. The background subtraction method is to use the difference method of the current image and background image to detect moving objects, with simple algorithm, but very sensitive to the changes in the external environment and has poor anti-interference ability.…”

“…To detect and track vehicles or pedestrians in real-time color histogram based technique is used. According to [11] a Gaussian Mixture Model is created to describe the color distribution within the sequence of images and to segment the image into background and objects. Object occlusion was handled using an occlusion buffer.…”

Object Matching using Skeletonization based on Hamming Distance

“…The AMF can be used to enhance the quality of noisy signals, in order to achieve better forcefulness in pattern recognition and adaptive control systems. These noise pixels are then substituted by the median pixel value of the pixels in the neighborhood that have passed the noise labeling test [16].…”