Efficient Hardware Implementation of Real-Time Object Tracking

Njuguna, Josphat Chege; Alabay, Emre; Çelebi, Anıl; Güllü, M. Kemal

doi:10.1109/siu55565.2022.9864768

Cited by 1 publication

(1 citation statement)

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“…Feature-based methods extract object features, such as edge information [ 49 ], color information [ 50 ], histogram of oriented gradients (HoG) [ 51 ], and Haar-like features [ 52 ], which distinguish the object from the background regions. Region-based methods, such as the Kernelized Correlation Filter (KCF) tracker [ 53 ] and the Minimum Output Sum of Squared Error (MOSSE) tracker [ 54 ], track objects by identifying regions in subsequent frames that correlate with the ROI in the previous frame. Additionally, methods like MeanShift [ 55 ] and CamShift [ 56 ] can be hybridized with the aforementioned algorithms to improve tracking efficiency by adjusting tracking windows to regions with high feature density.…”

Section: Related Workmentioning

confidence: 99%

High-Magnification Object Tracking with Ultra-Fast View Adjustment and Continuous Autofocus Based on Dynamic-Range Focal Sweep

Zhang,

Shimasaki,

Ishii

et al. 2024

Sensors

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Active vision systems (AVSs) have been widely used to obtain high-resolution images of objects of interest. However, tracking small objects in high-magnification scenes is challenging due to shallow depth of field (DoF) and narrow field of view (FoV). To address this, we introduce a novel high-speed AVS with a continuous autofocus (C-AF) approach based on dynamic-range focal sweep and a high-frame-rate (HFR) frame-by-frame tracking pipeline. Our AVS leverages an ultra-fast pan-tilt mechanism based on a Galvano mirror, enabling high-frequency view direction adjustment. Specifically, the proposed C-AF approach uses a 500 fps high-speed camera and a focus-tunable liquid lens operating at a sine wave, providing a 50 Hz focal sweep around the object’s optimal focus. During each focal sweep, 10 images with varying focuses are captured, and the one with the highest focus value is selected, resulting in a stable output of well-focused images at 50 fps. Simultaneously, the object’s depth is measured using the depth-from-focus (DFF) technique, allowing dynamic adjustment of the focal sweep range. Importantly, because the remaining images are only slightly less focused, all 500 fps images can be utilized for object tracking. The proposed tracking pipeline combines deep-learning-based object detection, K-means color clustering, and HFR tracking based on color filtering, achieving 500 fps frame-by-frame tracking. Experimental results demonstrate the effectiveness of the proposed C-AF approach and the advanced capabilities of the high-speed AVS for magnified object tracking.

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