An Effective Object Detection and Background Elimination Using Image Retrieval Method in Surveillance
Keywords:
Background subtraction, foreground identification, Gaussian Mixture model, Pearsonian family of distributionAbstract
Surveillance systems employing closed-circuit television (CCTV) cameras are extensively used in various security applications, including traffic monitoring, airports, and object tracking areas. Object tracking plays a crucial role in continuously monitoring objects of interest in an environment. The development of an effective object tracking system requires consideration of aspects such as object recognition, detection, and background elimination. Among these, object detection through CCTV cameras primarily relies on background subtraction techniques. This two-step approach involves building a statistical representation of the background scene and identifying the foreground by subtracting it from the image. However, the widespread use of CCTV cameras for security purposes has resulted in significant storage space requirements, raising concerns regarding global monitoring applications. To address these challenges, efficient models are needed to rapidly extract relevant images. However, data acquisition limitations due to illumination techniques, weather conditions, changing backgrounds, camera jitter, and baselines pose challenges to the effectiveness of retrieval systems, particularly in thermal cameras commonly used for background image acquisition in surveillance. This paper proposes a statistical model-based approach using the Pearsonian family of distribution, including its extensions, to enhance the efficiency of image retrieval and background identification. Considering the asymmetric nature of surveillance camera data, various families within the Pearsonian distribution are explored for effective background modeling in this study.
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