Object tracking‌ Sample Clauses

Object tracking‌. Input • A list of detected objects at each frame: each object defined by a 3D oriented bounding box Functionality • Track relevant objects and road users from the environment Output • A list of tracked objects: each object receiving an unique tracking ID and a relative speed Performance • Tracking ID persistence and relative speed accuracy Design The objects detected each frame from the 3D point cloud are provided in the form of oriented cuboids that can move in the lateral and longitudinal directions. The motion model assumed is the constant turn rate and velocity (CTRV) motion model, which has a state vector de- scribed by the vehicle lateral and longitudinal position, the velocity, heading and heading angle. Sensor measurements and the states of the tracked objects are modeled as random variables. The Bayes filtering is the main theory used in solving and modeling the object tracking problem by state prediction and state update. The Kalman Filter is based on the Bayesian method and it computes recursively the optimal parameter estimates from its pos- terior density. Two approaches exist: the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). The EKF uses a first order Xxxxxx series expansion approximation to linearize the process function. However, linearization using Jacobians is computationally ex- pensive so for this reason we have chosen the UKF, which makes an approximation based on the so called sigma point sampling. A data association procedure based on object’s 3D dimensions, orientation and location of the bounding box is employed for associating the tracks to the measurements.
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Object tracking‌. The output from the robust object detection is passed to a tracking module in order to perform robust and long term tracking, [cf. Objective 1]. Here the primary aim was to maintain robust tracks for all the individuals in the scene throughout the sequence in order to provide input into the higher level analysis such as threat analysis. Robust tracking refers to the real-time generation of reliable “tracklets”, while the long- term tracking refers to the processing of these tracklets to form complete tracks. The tracking module had two main roles: to produce a spatio-temporal detection of relevant entities – a track – and assign a unique ID tag to each track. As the track detection and ID track maintenance functionalities are closely intertwined, these aspects were jointly designed.

Related to Object tracking‌

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