Open AccessAccurate Tracking of Manoeuvring Target using Scale Estimation and Detection
Author(s) -
Zahir Ahmed Ansari,
M. J. Nigam,
Avnish Kumar
Publication year - 2019
Publication title -
defence science journal/defence science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.198
H-Index - 32
eISSN - 0976-464X
pISSN - 0011-748X
DOI - 10.14429/dsj.69.13042
Subject(s) - computer vision , computer science , tracking (education) , artificial intelligence , frame rate , smoothing , kalman filter , frame (networking) , inter frame , trajectory , fast fourier transform , algorithm , reference frame , psychology , telecommunications , pedagogy , physics , astronomy
Camera zoom operation and fast approaching/receding target causes scaling of acquired target in video frames. Fast moving target manifests in large inter-frame motion. In general, non-uniform background degrades performance of tracking algorithms. Fast Fourier transform (FFT)-based Correlation algorithms improve tracking in this scenario, but their applications is limited to small inter-frame motion. Increasing search region has implication on execution speed of the algorithms. Rapid target scaling, non-uniform background and large inter-frame motion of target hinder accurate and long term visual tracking. These challenges have been addressed for extended target tracking by augmenting fast discriminative scale space tracking (fDSST) algorithm with probable target location prediction and target detection. Localisation of fast motion has been achieved by applying fused outputs of Kalman filter and quadratic regression based prediction before applying fDSST. It has helped in accurate localisation of fast motion without increasing search region. In each frame, target location and size have been estimated using fDSST and further refined by target detection near this location. Smoothing and limiting of trajectory and size of detected target has enhanced tracking performance. Experimental results show considerable improvement of precision, success rate and centre location error tracking performance against state-of-the-art trackers in stringent conditions.