Backpack detection model using multi-scale superpixel and body-part segmentation

A backpack is a type of carried object (CO) widely used for various purposes because of its practicality. Various valuable items such as wallets, laptops, cameras, and cellphones may be kept in backpacks. Detecting backpacks in video surveillance is challenging due to their varying shapes, sizes, and colors. The process of localizing the area of the backpack in the image is a critical stage and dramatically influences the success of detection. This paper focuses on the process of localizing the backpack area through a multi-scale segmentation approach, where different scales are intended to detect the various size of the backpacks. Based on the assumption that the backpack is generally located above the bend line, the body-part method is then used to select superpixels. The selected superpixel feature is then extracted and used to train the model. Model testing is carried out in two scenarios. In the first scenario, the model is tested using the HOG (histogram of oriented gradients) feature, while in the second scenario, the model is tested using a combination of the HOG and histogram features. The experiment results show that on the DIKE20 dataset, the proposed model obtained an average F1 score of 69%. On PETS2006 and i-LIDS datasets, the proposed model shows an average F1 score of 68%, better than the average F1 score obtained by the state-of-the-art method.