The Kanade-Lucas-Tomasi (KLT) is one of the known tracking techniques that has gained much interest in the field of motion tracking. We began our study by performing a preliminary experiment to measure the perf
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Trajectory\(\Rightarrow\)Trajectory features are very informative (Zhou and Maskell2017). The global motion pattern in a scene can be utilized to describe a trajectory (Lin et al.2017). In general, the KLT (Kanade–Lucas–Tomasi feature tracker) is utilized to obtain the trajectories (Hariyon...
Two of the most popular feature extractors are the Harris corner detector (Harris & Stephens, 1988) followed by the Sum of Squared Difference (SSD) matching, and the Kanade-Lucas-Tomasi (KLT) tracker (Shi & Tomasi, 1994; Tomasi & Kanade, 1991). These methods can work well when the ...
high exposure to sun-light. We designed procedures for facilitating the annotation of the data necessary to train the proposed methods. To automate the annotation process between consecutive video frames, we utilised Kanade-Lucas-Tomasi (KLT)45, a feature-tracking algorithm, to track a set of poi...
· KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker [Project] · Tracking Cars Using Optical Flow[Project] · Secrets of optical flow estimation and their principles[Project] · implmentation of the Black and Anandan dense optical flow method[Project] ...
· KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker [Project] · Tracking Cars Using Optical Flow[Project] · Secrets of optical flow estimation and their principles[Project] · implmentation of the Black and Anandan dense optical flow method[Project] · Optical Flow Computation[...
Another advanced method is locating facial landmarks using the discriminative response map fitting (DRMF) method [30] and then applying kanade-lucas-tomasi (KLT) to track feature landmarks frame by frame. The alternative method solves coarse facial location and motion artifacts while still cannot ...
PKLTF [42] is a single-object long-term tracker that supports high appearance changes in the target, occlusions, and is also capable of recovering a target lost during the tracking process. PKLTF consists of two phases: The first one uses the Kanade Lukas Tomasi approach (KLT) [43] to ...
filtering, as well as false moving pixel removal based on independent motions of features. Hybrid target tracking includes Kanade-Lucas-Tomasi feature tracker (KLT) and meanshift, auto kernel scale estimation and updating, and consistently tracking in over-time using coherent motion of feature ...