Outline O Backgrounds and Introduction O Highlights and contribution O Proposal selection for Trackir EXperiment and result o Conclusion and Summary
Outline ⚫Backgrounds and Introduction ⚫Highlights and Contribution ⚫Proposal Selection for Tracking ⚫Experiment and Result ⚫Conclusion and Summary
Backgrounds Introduction Tracking-by-detection approaches are some of the most successful object trackers in recent years. Their success is largely determined by the detector model they learn initially and then update over time However, under challenging conditions where an object can undergo transformations like severe rotation, these methods are found to be lacking
Backgrounds & Introduction Tracking-by-detection approaches are some of the most successful object trackers in recent years. Their success is largely determined by the detector model they learn initially and then update over time. However, under challenging conditions where an object can undergo transformations like severe rotation, these methods are found to be lacking
Highlights and contribution In this paper, the author addresses this challenging problem by formulating it as proposal selection task and making two contributions The first one is introducing novel proposal estimated from the geometric transformations undergone by the object, and building a rich candidate set for predicting the object location The second one is devising a novel selection strategy using multiple cues like detection score and edgeness score computed from state-of-the irt obiect edges and motion boundaries
Highlights and Contribution In this paper, the author addresses this challenging problem by formulating it as proposal selection task and making two contributions: The first one is introducing novel proposal estimated from the geometric transformations undergone by the object, and building a rich candidate set for predicting the object location. The second one is devising a novel selection strategy using multiple cues like detection score and edgeness score computed from state-of-theart object edges and motion boundaries
Proposal Selection for Tracking The main components of the framework introduced by the passage for online object tracking are: Q learning the initial detector with a training set consisting of one positive sample, available as a bounding box annotation in the first frame and several negative boundil box samples which are automatically extracted from the entire image Then use Hog feature computed for these bounding boxes and learn the detector with a linear SVM, similar to other tracking- by-detection approaches. The detector is then evaluated on subsequent frames to estimate the candidate locations of the object
Proposal Selection for Tracking The main components of the framework introduced by the passage for online object tracking are: (i) learning the initial detector with a training set consisting of one positive sample, available as a bounding box annotation in the first frame, and several negative bounding box samples which are automatically extracted from the entire image. Then use HOG feature computed for these bounding boxes and learn the detector with a linear SVM, similar to other tracking-by-detection approaches. The detector is then evaluated on subsequent frames to estimate the candidate locations of the object
Proposal selection for Tracking (i building a rich candidate set of object locations in each frame consisting of proposals from the detector as well as the estimated geometric transformations represent the geometric transformation with a similarity matrix. The similarity transformation is defined by four parameters -one each for rotation and scale and two for translation. Then estimate them with a Hough transfor ting schem ing frame-to-frame optical flow correspondences
Proposal Selection for Tracking (ii) building a rich candidate set of object locations in each frame, consisting of proposals from the detector as well as the estimated geometric transformations represent the geometric transformation with a similarity matrix. The similarity transformation is defined by four parameters ---- one each for rotation and scale, and two for translation. Then estimate them with a Hough transform voting scheme using frame-to-frame optical flow correspondences