From Event: SPIE Optical Engineering + Applications, 2017
Current object tracking implementations utilize different feature extraction techniques to obtain salient features to track objects of interest which change in different types of imaging modalities and environmental conditions.nChallenges in infrared imagery for object tracking include object deformation, occlusion, background variations, and smearing, which demands high performance algorithms. We propose the directional ringlet intensity feature transform to encompass significant levels of detail while being able to track low resolution targets. The algorithm utilizes a weighted circularly partitioned histogram distribution method which outperforms regular histogram distribution matching by localizing information and utilizing the rotation invariance of the circular rings. The image also utilizes directional edge information created by a Frei-Chen edge detector to improve the ability of the algorithm in different lighting conditions. We find the matching features using a weighted Earth Movers Distance (EMD), which results in the specific location of the target object. The algorithm is fused with image registration, motion detection from background subtraction and motion estimation from Kalman filtering to create robustness from camera jitter and occlusions. It is found that the DRIFT algorithm performs very well under different operating conditions in IR imagery and yields better results as compared to other state-of-the-art feature based object trackers. The testing is done on two IR databases, a collected database of vehicle and pedestrian sequences and the Visual Object Tracking (VOT) IR database.
Evan Krieger, Theus Aspiras, and Vijayan K. Asari, "Directional ringlet intensity feature transform (DRIFT) based object tracking in IR imagery," Proc. SPIE 10403, Infrared Remote Sensing and Instrumentation XXV, 104030L (Presented at SPIE Optical Engineering + Applications: August 08, 2017; Published: 30 August 2017); https://doi.org/10.1117/12.2275550.
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