This thesis focuses on employing commonly used object detection algorithms for traffic video monitoring under privacy-preserved conditions. The deidentification of the data was achieved by mechanically defocusing the camera used for image acquisition. Blurred images and their sharp counterparts were then used to train and test the efficiency of different object detection methods.
Within the scope of this thesis, four distinct algorithms were employed for object detection in images: DETR (ang. Detection Transformer), Faster R-CNN, YOLOv3, and HRNet. DETR introduces a novel approach that utilizes transformers for direct object detection in images without needing prior object proposals. Faster R-CNN was chosen for its speed and accuracy in object detection. YOLOv3 is known for its efficiency in real-time object detection, and HRNet is recognized for its precision in object segmentation, particularly in high-resolution images.
The annotated image database used in this research includes thousands of images featuring cars, buses, and vans, all manually annotated with precise bounding boxes to define the position and size of objects within the images. The database categorizes these images into three blurriness levels: clear, moderately blurred, and heavily blurred. This categorization enables a systematic examination of algorithm performance across different levels of image blurriness.
The thesis concludes with both qualitative and quantitative analyses. The quantitative analysis involves reviewing images and manually analyzing the outcomes, whereas a software program performs the qualitative analysis, executing analytical computations of object detection efficacy. This comprehensive evaluation provides insights into the performance and capabilities of each algorithm under various levels of image blurriness.
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