Convolutional Recurrent Predictor: Implicit Representation for Multi-Target Filtering and Tracking

Sensor management for multi-target tracking via Multi-Bernoulli filtering

In multi-object stochastic systems, the issue of sensor management is a theoretically and computationally challenging problem. In this paper, we present a novel random finite set (RFS) approach to the multi-target sensor management problem within the partially observed Markov decision process (POMDP) framework. The multi-target state is modelled as a multi-Bernoulli RFS, and the multi-Bernoulli...

Particle Filtering for Multi-target Tracking and Sensor Management

Multi-Target Tracking Using Hierarchical Convolutional Features and Motion Cues

In this paper, the problem of multi-target tracking with single camera in complex scenes is addressed. A new approach is proposed for multi-target tracking problem that learns from hierarchy of convolution features. First fast Region-based Convolutional Neutral Networks is trained to detect pedestrian in each frame. Then cooperate it with correlation filter tracker which learns target’s appeara...

Convolutional Gating Network for Object Tracking

Object tracking through multiple cameras is a popular research topic in security and surveillance systems especially when human objects are the target. However, occlusion is one of the challenging problems for the tracking process. This paper proposes a multiple-camera-based cooperative tracking method to overcome the occlusion problem.  The paper presents a new model for combining convolutiona...

Particle Filtering For Target Tracking

Particle filtering is a sequential Monte Carlo technique that recursively computes the posterior probability density function using the concept of “Importance Sampling”. This paper considers the application of particle filtering technique to a target tracking application, in which a radar sends a signal towards a target and estimates the state (position and velocity) of the target using the obs...