Moving Object Detection and Tracking Algorithm Using Hybrid Decomposition Parallel Processing

Abstract

Moving object detection, classification and tracking are more crucial and challenging task in most of the computer vision and machine vision applications such as robot navigation, human behavior analysis, traffic flow analysis and etc. However, most of object detection and tracking algorithms are not suitable for real time processing and causes slower processing speed due to the processing and analyzing of high resolution video from high-end multiple cameras. It requires more computation and storage. To address the aforementioned problem, this paper proposes a way of parallel processing of temporal frame differencing algorithm for object detection and contour tracking using the mixture of functional and domain decomposition parallel processing techniques. It has two main contributions. First, steps of frame differencing are parallelized using functional decomposition technique. Second, the processing of frames in each steps of frame differencing is again parallelized using domain decomposition technique. Finally, the performance is evaluated in Aneka Private Cloud platform and which yields to detect and track the object very swiftly and accurately.

A Cost-Effective Method for Detecting and Tracking Moving Objects using Overlapping Methods

“…The display of comprehensive features can be effectively removed by combining various visual property aspects. An innovative component-based method for object detection on a two-dimensional image and its use as a visual landmark has been presented by researchers in [20]. Object recognition is a hybrid cryptographic system that makes it possible to keep track of the topology and use it to power the recognition procedure.…”

“…A comparison of earlier investigations is provided in Table I. The simple and fast method Digital images learning-statistics conversion [18] The simplest and most suitable for dark images driving Image segmentation, color composition and topology Spatial [19] Simple method Identifying small objects Contour model Spatial [8] Simple method Moving Pictures Creating and testing hypothesis and verification step Spatial [20] It provides a better visual effect Digital images meta-heuristic algorithm Spatial [21] It provides a better signal than noise Satellite Images High-pass and low-pass filtering Spatial [22] III. PROPOSED METHOD…”