Pickpocketing Recognition in Still Images

Human activity recognition (HAR) is a challenging topic in the computer vision field. Pickpocketing is a type of human criminal actions. It needs extensive research and development for detection. This paper researches how it’s possible of pickpocketing recognition in still images. This paper takes consideration both of classification and detection. We develop our models from state-of-art pre-trained models: VGG16, ResNet50, ResNet101, and ResNet152. Moreover, we also include a convolutional block attention module (CBAM [27]) in the model. The attention mechanism enhances model performances by focusing on informative features. For classification, the highest accuracy (89%) is ResNet152 with CBAM [27] (ResNet152+CBAM). We also examine pickpocketing detection on RetinaNet [14] and YOLOv.3 [34]. The mean average precision (mAP) of pickpocketing detection is consistent with Redmon et al. [34]. RetinaNet’s precision (80 mAP) is higher than YOLOv.3 (78 mAP), but YOLOv.3 is much faster detection. ResNet152+CBAM model detection on RetinaNet approach provides the highest mAP. However, it is much slower detection than YOLOv.3 (only 10 ms). This paper proves that It is possible to implement pickpocketing on still images in a reliable time and with outstanding accuracy. This proposed model possibly apply to the other HAR tasks.