Abstract
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.
Highlights
Automatic detection of moving objects is a key task in numerous fields, such as video surveillance, and computer vision
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
Denote with I(i, j, n) the intensity of an IR video sequence acquired by a steady camera, where i and j indicate the position within each frame and n indicates the instant of acquisition
Summary
Automatic detection of moving objects is a key task in numerous fields, such as video surveillance, and computer vision. Strategies based on temporal analysis aim to evaluate if any significant change occurs in the scene over time They are suitable to detect moving objects, since they inherently exploit the motion of the objects. An interesting approach of linear filtering has been proposed in [29], where the background is estimated by means of a first order recursive filter, capable to compensate illumination changes and highly performing in terms of computational time Such an approach has been designed for optical video sequences, for tracking the human body when it is clearly visible in the scene, it has not been tested in the IR domain and with small and dim objects.
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