Abstract:
This thesis presents an innovative and efficient approach to track multiple objects in a
video sequence by integrating state-of-art You Only Look Once (YOLO) object detection
framework with efficient Reduced Order Observer (ROO) based target trajectory estimator.
Traditionally, Kalman Filter is utilized to estimate detected objects in video frames, however,
this approach is computationally demanding due to repeated estimation of large number of
state variables in each iteration. To address this challenge, ROO based trajectory estimator is
proposed that focuses on estimating a subset of state vector, thereby enhancing processing
speed for real-time applications.
A video sequence with frame rate of 30 frames per second is fed into YOLO model
which outputs bounding box defined by a state vector for each detected object in a frame.
State vector elements include position (x and y axis), velocity (x and y axis), aspect ratio and
height of bounding box. State estimator is required to estimate states of these bounding boxes
/ detected objects in next frame of video sequence. ROO separates observable states from
unmeasurable states and only estimates unmeasurable states. These estimates combined with
Intersection Over Union (IoU) matching are used to assign bounding boxes / detected objects
to tracklets ensuring efficient tracking even in the presence of occlusion and dynamic real time environments.
This approach has been validated by implementing ROO framework in MATLAB
R2023b and subsequently in Microsoft Visual Studio for online tracking of objects in video
frame. This research contributes to the field of multi-object tracking by providing a
computationally efficient trajectory estimator with potential applications in autonomous
driving, surveillance and robotics.
