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Accurate Tracking by Overlap MaximizationBhat, Goutam January 2019 (has links)
Visual object tracking is one of the fundamental problems in computer vision, with a wide number of practical applications in e.g.\ robotics, surveillance etc. Given a video sequence and the target bounding box in the first frame, a tracker is required to find the target in all subsequent frames. It is a challenging problem due to the limited training data available. An object tracker is generally evaluated using two criterias, namely robustness and accuracy. Robustness refers to the ability of a tracker to track for long durations, without losing the target. Accuracy, on the other hand, denotes how accurately a tracker can estimate the target bounding box. Recent years have seen significant improvement in tracking robustness. However, the problem of accurate tracking has seen less attention. Most current state-of-the-art trackers resort to a naive multi-scale search strategy which has fundamental limitations. Thus, in this thesis, we aim to develop a general target estimation component which can be used to determine accurate bounding box for tracking. We will investigate how bounding box estimators used in object detection can be modified to be used for object tracking. The key difference between detection and tracking is that in object detection, the classes to which the objects belong are known. However, in tracking, no prior information is available about the tracked object, other than a single image provided in the first frame. We will thus investigate different architectures to utilize the first frame information to provide target specific bounding box predictions. We will also investigate how the bounding box predictors can be integrated into a state-of-the-art tracking method to obtain robust as well as accurate tracking.
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Uncertainty Estimation and Confidence Calibration in YOLO5FaceSavinainen, Oskar January 2024 (has links)
This thesis investigates predicting the Intersection over Union (IoU) in detections made by the face detector YOLO5Face, which is done to use the predicted IoU as a new uncertainty measure. The detections are done on the face dataset WIDER FACE, and the prediction of IoU is made by adding a parallel head to the existing YOLO5Face architecture. Experiments show that the methodology for predicting the IoU used in this thesis does not work and the parallel prediction head fails to predict the IoU and instead resorts to predicting common IoU values. The localisation confidence and classification confidences of YOLO5Face are then investigated to find out which confidence measure is least uncertain and most suitable to use when identifying faces. Experiments show that the localisation confidence is consistently more calibrated than the classification confidence. The classification confidence is then calibrated with respect to the localisation confidence which reduces the Expected Calibration Error (ECE) for classification confidence from 0.17 to 0.01.
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