Outdoor object detection for surveillance based on modified GMM and Adaptive Thresholding
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ORIGINAL RESEARCH
Outdoor object detection for surveillance based on modified GMM and Adaptive Thresholding Navneet S. Ghedia1
•
C. H. Vithalani2
Received: 10 September 2019 / Accepted: 26 September 2020 Bharati Vidyapeeth’s Institute of Computer Applications and Management 2020
Abstract This paper presents a modified Gaussian Mixture Model (GMM) and Adaptive Thresholding designed to improve object detection accuracy for the outdoor surveillance. Intrinsic and extrinsic improvements in traditional GMM will handle the outdoor dynamic scenes e.g. tree weaving, gradual illumination changes, partial occlusions and also handles certain amount of shadow. For foreground detection, Adaptive Thresholding is utilized for better classification among the background and foreground objects and it also help to reduces false positives and hence increases the detection accuracy. We tested proposed algorithm on standard datasets consisting of CDnet 2014, PETS 2009 and ViSOR. The robustness of the propose algorithm has been compared with the ground truth and other similar approaches through several performance evaluation metrics. The experimental results conclude that the proposed algorithm efficiently detect objects in dynamic environments as well as handle partial occlusions and certain amount of shadows very efficiently. Keywords Gaussian mixture model Foreground detection Adaptive Thresholding Dynamic background
& Navneet S. Ghedia [email protected] C. H. Vithalani [email protected] 1
Sanjaybhai Rajguru College of Engineering-Rajkot, Rajkot, India
2
Government Engineering College-Rajkot, Rajkot, India
1 Introduction Enhancing and Ensuring a fair level of security across multiple scales of time and space in public places such as airport, railway station and at other places becomes extremely multifarious challenge for smart video surveillance system and it also enhances situational consciousness. There are multiple security challenges like screening system, database system, biometric system and video surveillance system for object tracking and verifying identity and also to monitor activities respectively. Today video surveillance system focuses on compression of data for the purpose of storing and transmission. Locating, identifying and learning the object behaviour in video sequence requires two main steps. • •
Detection of objects–foregrounds that are in motion. Object’s behaviour recognition.
Smart video surveillance system queries fast and robust algorithm for estimating background, motion segmentation, object tracking, scene analysis and also assist operator for important scene events. Smart and intelligent video surveillance is the most researched topic for the last decade because more importance is given to security and military applications [1]. T. Reeve [2] and Rajiv Shah [3] have beautifully surveyed the penetration and importance of the surveillance system in United Kingdom and United States. They have reviewed that the large amount of surveillance data monitoring was done by human o
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