Modified M‐RCNN approach for abandoned object detection in public places

Abstract

AbstractDetection of abandoned and stationary objects like luggage, boxes, machinery, and so forth, in public places is one of the challenging and critical tasks in the video surveillance system. These objects may contain weapons, bombs, or other explosive materials that threaten the public. Though various applications have been developed to detect stationary objects, different challenges, like occlusions, changes in geometrical features of things, and so forth, are still to be addressed. Considering the complexity of scenarios in public places and the variety of objects, a context‐aware model is developed based on mask region‐based convolution network (M‐RCNN) for detecting abandoned objects. A modified convolution operation is implemented in the Backbone network to understand features from geometric variations near objects. These modified operation layers can be adapted based on geometric interpretations to extract required features. Finally, a bounding box operation is performed to locate the abandoned object and mask the particular thing. Experiments have been performed on the benchmark dataset like ABODA and our dataset, which shows that an mAP of 0. 0.699 is achieved for model 1, 0.675 is achieved for model 2, and 0.734 mAP is completed for model 3. An ablation analysis has also been performed and compared with other state‐of‐the‐art methods. Based on the results, the proposed model better detects abandoned objects than existing state‐of‐the‐art methods.