The customer is a North American start-up providing automatic security solutions for businesses and government institutions. The company actively adopted a machine learning approach for real-time object detection and action recognition tasks in video streams from their customers’ security cameras.

Challenge

The customer’s company was developing a brand new security solution for outdoor surveillance. MERA, a division of Orion, was tasked to create a PoC (proof of concept) for action recognition using machine learning techniques. MERA experts had to deliver the solution at short notice operating with limited resources, including both design resource for implementation and hardware resources for the selected platform.

Solution

Bearing in mind limited hardware capabilities and lack of dataset provided by the customer, MERA engineers conducted a brief study to determine potential solutions which can show appropriate results with a limited dataset. Several neural network architectures were selected as possible candidates for PoC, including: activity detection based on the single frame using CNN, activity detection using multiple frames. The last option showed more promising results during experiments. Therefore, the MERA team had checked a few other options: 3DCNN and LSTM.

Eventually, the most encouraging results were demonstrated by an approach combining transfer learning (using pre-trained Inception v3 model) and LSTM. This model was selected as the final solution for PoC.

Impact

MERA provided the customers with the reliable Proof of Concept they needed for further solution development. Our experts successfully accomplished the task in as little as 3 months, despite resource constraints and lack of dataset. The models were integrated into the web server allowing the customers to perform live-action demonstrations for their clients.

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