Abstract
Abstract. Detecting and tracking objects in video has been as a research area of interest in the field of image processing and computer vision. This paper evaluates the performance of a novel method for object detection algorithm in video sequences. This process helps us to know the advantage of this method which is being used. The proposed framework compares the correct and wrong detection percentage of this algorithm. This method was evaluated with the collected data in the field of urban transport which include car and pedestrian in fixed camera situation. The results show that the accuracy of the algorithm will decreases because of image resolution reduction.
Highlights
Object detection is an important task within the field of computer vision due video surveillance, traffic monitoring, vehicle navigation, robotics, 3D reconstruction and content based indexing and retrieval
Mahesh et al in (Pawaskar, Narkhede et al 2014) proposed a "fast and reliable algorithm for moving object detection". It provides the implementation of an efficient object detection algorithm that can be employed in real time embedded systems due to its fast processing
circuit television (CCTV) refers to all cameras that are fixed in place and the images are sent to one or more locations
Summary
Object detection is an important task within the field of computer vision due video surveillance, traffic monitoring, vehicle navigation, robotics, 3D reconstruction and content based indexing and retrieval. Mahesh et al in (Pawaskar, Narkhede et al 2014) proposed a "fast and reliable algorithm for moving object detection" It provides the implementation of an efficient object detection algorithm that can be employed in real time embedded systems due to its fast processing. First it will detect moving object based on background subtraction. Darshan et al presented an algorithm for Detection and Tracking of Multiple Moving Objects in Real Time in (Barcellos, Bouvié et al 2015) This method is robust in various environments including indoor and outdoor scenes and different types of background scenes, because it uses edge-based features and clustering is used which make it insensitive to illumination changes. The rest of this paper is organized as follows: Section II describes the main process of moving object detection, section III presents evaluation of moving object detection while section IV consists of the conclusion part
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