Multiple object tracking using feature fusion in hierarchical LSTMs

A hierarchical feature fusion framework for adaptive visual tracking

a r t i c l e i n f o A Hierarchical Model Fusion (HMF) framework for object tracking in video sequences is presented. The Bayesian tracking equations are extended to account for multiple object models. With these equations as a basis a particle filter algorithm is developed to efficiently cope with the multi-modal distributions emerging from cluttered scenes. The update of each object model ta...

Object Tracking Based on Camshift with Multi-feature Fusion

It is very hard for traditional Camshift to survive of drastic interferences and occlusions of similar objects. This paper puts forward an innovative tracking method using Camshift with multi-feature fusion. Firstly, SIFT features and edge features of the Camshift in RGB space are counted to reduce the probability of disruption by occlusion and clutter. Then, the texture features are collected ...

Object Tracking Using Piecewise Feature Clustering

Object tracking is a complex, yet essential task to be addressed in any video surveillance application. Many real-time techniques proposed in the literature rely on a frame-to-frame matching of objects. This paper describes a technique which takes into consideration the inherent temporal coherence that exists across frames, thus being able to robustly perform tracking while handling difficult s...

Real-Time Object Tracking Using Colour Feature

Video Tracking is the process of locating a moving object over time using a camera. The objective of video tracking is to associate target objects in consecutive video frames. The association can be especially difficult when the objects are moving fast relative to the frame rate. Another situation that increases the complexity of the problem is when the tracked object changes orientation over t...

Feature Based Object Tracking Using PTZ Camera