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Real-time detecting moving objects in infrared video sequences may be particularly challenging because of the characteristics of the objects, such as their size, contrast, velocity and trajectory. Many proposed algorithms achieve good performances but only in the presence of some specific kinds of objects, or by neglecting the computational time, becoming unsuitable for real-time applications. To obtain more flexibility in different situations, we developed an algorithm capable of successfully dealing with small and large objects, slow and fast objects, even if subjected to unusual movements, and poorly-contrasted objects. The algorithm is also capable to handle the contemporary presence of multiple objects within the scene and to work in real-time even using cheap hardware. The implemented strategy is based on a fast but accurate background estimation and rejection, performed pixel by pixel and updated frame by frame, which is robust to possible background intensity changes and to noise. A control routine prevents the estimation from being biased by the transit of moving objects, while two noise-adaptive thresholding stages, respectively, drive the estimation control and allow extracting moving objects after the background removal, leading to the desired detection map. For each step, attention has been paid to develop computationally light solution to achieve the real-time requirement. The algorithm has been tested on a database of infrared video sequences, obtaining promising results against different kinds of challenging moving objects and outperforming other commonly adopted solutions. Its effectiveness in terms of detection performance, flexibility and computational time make the algorithm particularly suitable for real-time applications such as intrusion monitoring, activity control and detection of approaching objects, which are fundamental task in the emerging research area of Smart City

A Flexible Algorithm for Detecting Challenging Moving Objects in Real-Time within IR Video Sequences