Multimodal ranking for non-compliance detection in retail surveillance

In retail stores, cashier non-compliance activities at the Point of Sale (POS) are one of the prevalent sources of retail loss. In this paper, we propose a novel approach to reliably rank the list of detected non-compliance activities of a given retail surveillance system, thereby provide a means of significantly reducing the false alarms and improving the precision in non-compliance detection. Our approach represents each detected non-compliance activity using multi-modal features coming from video data, transaction logs (TLog) data and intermediate results of the video analytics. We then learn a binary classifier that successfully separate true positives and false positives in a labeled training set. A confidence score for each detection can then be computed using the decision value of the trained classifier, and a ranked list of detections can be formed based on this score. The benefit from having this ranked list is two-fold. First, a large number of false alarms can be avoided by simply keeping the top part of the list and discarding the rest. Second, a trade off between precision and recall can easily be performed by sliding the discarding threshold along this ranked list. Experimental results on a large scale dataset captured from real stores demonstrate that our approach achieves better precision than a state-of-the-art system at the same recall. Our approach can also reach an operating point that exceeds the retailers´ expectation in terms of precision, while retaining an acceptable recall of more than 60%.