Real-time object tracking is a problem which involves extraction of critical information from
complex and uncertain imagedata. In this paper, we present a comprehensive methodology …

Real-time object tracking is a problem which involves extraction and processing of critical
information from complex and uncertain image data in a very short time. In this study, we …

Object tracking algorithms typically leverage on either or both appearance and motion
features of target (s). It is common in multi-object tracking to use both features, whereas the …

In this paper, we present a comprehensive study to design an artificial neural network (ANN)
for tracking a target in an image sequence. The proposed ANN architecture is a single …

In this paper, we research the real-time object tracking technology. The object tracking
algorithm discussed in this paper is developed based on the Tracking-Learning-Detection …

To address the poor image feature extraction ability, excessive tracking time, and low
accuracy of traditional real-time algorithms for aerial tracking of vehicle traffic, a real-time …

We present deep neural network models applied to tracking objects of interest. Deep neural
networks trained for general-purpose use are introduced to conduct long-term tracking …

Discriminative correlation filter (DCF) based tracking algorithms have recently achieved
excellent performance in challenging factors such as rotations and distractors. However, in …

Tracking an object in night surveillance video is a challenging task as the quality of the
captured image is normally poor with low brightness and contrast. The task becomes harder …

In this paper, an improved method for object tracking is proposed using Radial Basis
Function Neural Networks. Optimized k-means color segmentation is employed for detecting …