Design and implementation of colour texture‐based multiple object detection using morphological gradient approach

Abstract

SummaryNon‐rigid moving multiple objects detection and tracking play an important role in intelligent video surveillance system, autonomous navigation, and activity analysis. Closed Circuit Television (CCTV) systems are deployed in numerous areas such as airports, traffic intersections, underground stations, mass events, mall, schools, and organisations for security and public surveillance. Although these cameras record continuous video 24x7, it is a human constraint to manually monitor all events such as crime, terrorism, hideous, suspicious activities, the positioning of the vehicle, and fire recorded from a number of cameras. Moreover, problems like dynamic background, the creation of ghost, sensor noise, varying illumination, and colour and compression artefacts affect effective detection of multiple moving objects. This study presents an effective approach named as enhanced Fractal Texture Analysis with KNN classifier (FTAKC) for tracking and detection of multiple objects from a video sequence. The proposed approach comprises three main phases, namely, detection of moving object, tracking of the object (enhanced Fractal Texture Analysis), and behaviour analysis for activity recognition (KNN classifier). The image feature has been extracted based on colour, texture, and geometry were used to identify and track multiple objects in video frames, and Problem domain knowledge rules were applied to distinguish normal or anomalous activities as well as behaviours. Edge detection algorithm (Intersection over Union (IoU) threshold to determine possible edge connections) was applied toward enhancing the illumination variation by multi‐block Local Binary Pattern (LBP) temporal‐analysis to do the segmentation. Finally, the efficiency and effectiveness of the proposed approach has been estimated based on the measure of average PSNR, precision, recall, f‐measure, accuracy, and execution time. The Laboratory for Image and Media Understanding (LIMU) dataset has been utilised toward illustrating the robustness of the proposed approach. Furthermore, it evaluated the performance based on the measure of precision, recall, and F‐measure metrics. It has been tentatively demonstrated that the proposed approach is suitable for recognizing multiple moving object with detection accuracy up to 93.56%. The simulated results show that suggested approach is robust, flexible, as well as able to outperform the traditional methods than the present object detection method.