Retail video analytics to identify consumer behaviour

Make your store popular, friendly and the most sought after by analyzing your consumer sentiments and feelings with retail video analytics


Problem statement

Businesses need retail analytics for effective store management. Big stores have the need to track customer behaviour to provide the best service to them. They also want to improve their products so that they remain the first choice for the consumers. The client was a grocery chain that wanted to understand consumer behaviour from their CCTV video recordings. They wanted us to build a system that can analyze the store aisles recordings to assess consumer behaviour based on some metrics. For example, the customer footfall metric helps them to equip their store with adequate support staff to service their customers. To build the system, we had to study shoppers with metrics namely, gender, age, products of interest according to their age to build a profile for the shoppers frequenting the store. These metrics help estimate the customer footfall, consumer-type estimation, dwell time etc.

Our solution

This project involves the integration of various Computer Vision algorithms, person detection, face emotion recognition, age - gender detection and custom object detection. Of all the above-said algorithms, only the custom object detection of a retail stack of consumables required custom training. For the rest of the algorithms, we integrated pre-built models from OpenVINO toolkit from Intel. The data acquisition phase of the project involved collecting images of the retail stack of consumables under varying lighting conditions and camera noise conditions. This was then followed by an object annotation phase with the CVAT tool to generate a segmentation dataset of 2000 images. We implemented a neural network training process derived from the CNN architectures including the Yolo, Xception and Inception. From our RoC studies, we observed that the Yolo V2 Lite model was performing adequately in terms of both accuracy and execution time. We then developed a CCTV video stream processing application that integrates the output from the various object detection algorithms and implemented a robust reporting visualization program. The video stream processing server was then deployed as an on-premise installation that integrated with their existing CCTV NVR system.

Key metrics

We are able to introduce a novel retail analytics process for their grocery store operation which gave unique insights about consumer interests and buying patterns. We developed this project in a time frame of 30 weeks. The CCTV video analytics server was able to give insights to our clients about the ideal grocery store layout and manpower scheduling. They were able to attract 15% more shoppers by making layout and manpower changes to their grocery chain.

Technology stack

Logo for tensor flow library Logo for OpenCV image processing library

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