Identifying threatful situations involving individuals is the challenging domain of individual safety from falls off the perimeter of a facility or a ship. Our unique offering consists in the development of a data fusion middleware that builds on an innovative set of image recognition and motion detection algorithms, aimed at lowering false alarms and achieving accurate detection of real (human-shaped) targets.

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We selected a combination of non-determinism and voting among the classifiers, whereby many different weighted mean values are computed, whose weights are produced by a probability function, and the sum of the medians that exceeds the threshold is computed, leading to a possible alert condition. Our promise is that the system operates better than any one of the individual classifiers, and additionally minimizes false alarms.

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The obvious problem in such applications is the lack of training data representative of the multitude of conditions observed. Additionally the operation of any detection system at sea is dramatically affected by the weather conditions (rain, mist, fog, hail), the variable background imagery and the movement of the platform itself. In our implementation, the probability function that produces the weights may adapt to changes in the environment (e.g. rain that negatively affects the visibility of the camera), so that other, more reliable metrics get higher importance.

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The applications envisaged for the future include the perimeter protection, including border protection, facility intrusion detection, protection of maritime piracy at international waters.

 

Applications to semi-Autonomous vehicles

The developed system may also be adapted for use by semi-autonomous vehicles for enhancing the safety of pedestrians, bikers and live animals including pets, in a way similar to Autonomous Pedestrian Detection systems currently evaluated by EuroNCAP.