Intensity variation function and template matching-based pedestrian tracking in infrared imagery with occlusion detection and recovery

Robustly and efficiently tracking pedestrians in the infrared spectrum is a crucial requirement for a number of applications. At the same time, it is also particularly critical due to both the peculiarities of infrared images and pedestrian targets. In fact, low resolutions and high signal-to-noise ratios combined with extremely variable target signatures, chaotic trajectories, and frequent occlusions have forced researchers to develop ever more complex strategies characterized by a neat trade-off between tracking accuracy and computational complexity. Thus, most of the existing techniques might not be capable of ensuring real-time performances with a suitable degree of robustness, especially on limited-resource hardware used, e.g., in automotive or security scenarios. We present a technique that extends an extremely efficient tracking method originally tailored to targets that exhibit a clear and stable hot spot to allow it to deal with pedestrian targets by reusing its core components and integrating an occlusion detection and recovery mechanism. Experimental results obtained on public datasets confirmed that the devised method is able to obtain a robustness that is superior to that of other common approaches by maintaining the high tracking speed of the reference method.