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Deep Learning for UAV-based Object Detection and Tracking: A Survey Xin Wu, Wei Li, Danfeng Hong, Ran Tao, Qian Du Owing to effective and flexible data acquisition, unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). vision tasks: object detection, single object tracking, and multiple object tracking. Then, a detailed quantitative study is performed using most re-cent state-of-the-art algorithms for each task. Experimental results show that the current state-of-the-art methods perform relative worse on our dataset, due to the new challenges appeared in UAV There are three key steps in video analysis, detection interesting moving objects, tracking of such objects from each and every frame to frame, and analysis of object tracks to recognize their behavior.Therefore, the use of object tracking is pertinent in the tasks of, motion based recognition. Automatic detection, tracking, and counting 1 Length: 266 pages. Edition: 1. Language: English. Publisher: Chapman and Hall/CRC. Publication Date: 2022-11-03. Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields. Figure 1 The Basic flow diagram of Object tracking Studies related to object detection The detection of an object in video sequence plays a significant role in many applications. Specifically as video surveillance applications (Amandeep and Goyal, 2015). The different types of object detection are shown in figure 2. Target object tracking. Once the target objects are detected using a detection method, the positions of the objects should be tracked in the subsequent frames. Two vision-based object tracking methods have been reviewed and their feasibilities in our application have been analyzed. 3.3.1. Optical flow tracking This paper advocates the exploitation of the Polar coordinate system and proposes a new Polar Transformer ( PolarFormer) for more accurate 3D object detection in the bird's-eye-view (BEV) taking as input only multi-camera 2D images. 3 Highly Influenced PDF View 5 excerpts, cites methods and background In the tracking-by-detection framework, object tracking and object detection are considered separately [ 9, 10 ]. However, in our proposed method, tracking is based on features from the detector, which are jointly optimized for detection and association. Denote the CNN features as F, the i th trajectory as T_ {i}, and the j th detection as d_ {j}. First, anchor-based one-shot trackers such as Track R-CNN (Voigtlaender et al. 2019) overlook the re-ID task because they need anchors to first detect objects (i.e. using RPN Ren et al. 201