Multitarget tracking with split and merged measurements (original) (raw)

On Detection, Data Association and Segmentation for Multi-Target Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019

In this work, we propose a tracker that differs from most existing multi-target trackers in two major ways. Firstly, our tracker does not rely on a pre-trained object detector to get the initial object hypotheses. Secondly, our tracker's final output is the fine contours of the targets rather than traditional bounding boxes. Therefore, our tracker simultaneously solves three main problems: detection, data association and segmentation. This is especially important because the output of each of those three problems are highly correlated and the solution of one can greatly help improve the others. The proposed algorithm consists of two main components: structured learning and Lagrange dual decomposition. Our structured learning based tracker learns a model for each target and infers the best locations of all targets simultaneously in a video clip. The inference of our structured learning is achieved through a new Target Identity-aware Network Flow (TINF), where each node in the network encodes the probability of each target identity belonging to that node. The probabilities are obtained by training target specific models using a global structured learning technique. This is followed by proposed Lagrangian relaxation optimization to find the high quality solution to the network. This forms the first component of our tracker. The second component is Lagrange dual decomposition, which combines the structured learning tracker with a segmentation algorithm. For segmentation, multi-label Conditional Random Field (CRF) is applied to a superpixel based spatio-temporal graph in a segment of video, in order to assign background or target labels to every superpixel. We show how the multi-label CRF is combined with the structured learning tracker through our dual decomposition formulation. This leads to more accurate segmentation results and also helps better resolve typical difficulties in multiple target tracking, such as occlusion handling, ID-switch and track drifting. The experiments on diverse and challenging sequences show that our method achieves superior results compared to competitive approaches for detection, multiple target tracking as well as segmentation.

Multiple Hypothesis Target Tracking Using Merge and Split of Graph’s Nodes

Lecture Notes in Computer Science, 2006

In this paper, we propose a maximum a posteriori formulation to the multiple target tracking problem. We adopt a graph representation for storing the detected regions as well as their association over time. The multiple target tracking problem is formulated as a multiple paths search in the graph. Due to the noisy foreground segmentation, an object may be represented by several foreground regions and one foreground region may corresponds to multiple objects. We introduce merge, split and mean shift operations that add new hypothesis to the measurement graph in order to be able to aggregate, split detected blobs or re-acquire objects that have not been detected during stop-and-gomotion. To make full use of the visual observations, we consider both motion and appearance likelihood. Experiments have been conducted on both indoor and outdoor data sets, and a comparison has been carried to assess the contribution of the new tracker.

Point target probabilistic multiple hypothesis tracking

IET Radar, Sonar & Navigation, 2011

Probabilistic Multiple Hypothesis Tracking (PMHT) is an algorithm for multi-target tracking in clutter with computational requirements, which are linear in the number of targets and the number of measurements. In order to achieve this, the PMHT removes the point target constraint, and uses the expectation maximisation procedure to optimise both data association probabilities and the target trajectory state estimates. However, PMHT is known to have high track-loss percentage in comparison with Probabilistic Data Association, at least in point target tracking. The authors propose a new PMHT-like algorithm to solve some problems of PMHT. In this study they revert the point target constraint. The authors call the new algorithm Point target PMHT. Simulation results show the efficiency of this method.

Multitarget Tracking using the Joint Multitarget Probability Density

This work addresses the problem of tracking multiple moving targets by recursively estimating the joint multitarget probability density (JMPD). Estimation of the JMPD is done in a Bayesian framework and provides a method for tracking multiple targets which allows nonlinear target motion and measurement to state coupling as well as non-Gaussian target state densities. The JMPD technique simultaneously estimates both the target states and the number of targets in the surveillance region based on the set of measurements made. We give an implementation of the JMPD method based on particle filtering techniques and provide an adaptive sampling scheme which explicitly models the multitarget nature of the problem. We show that this implementation of the JMPD technique provides a natural way to track a collection of targets, is computationally tractable, and performs well under difficult conditions such as target crossing, convoy movement, and low measurement signal-to-noise ratio (SNR).

Multiple hypothesis tracking algorithm for multi‐target multi‐camera tracking with disjoint views

IET Image Processing, 2018

In this study, a multiple hypothesis tracking (MHT) algorithm for multi-target multi-camera tracking (MCT) with disjoint views is proposed. The authors' method forms track-hypothesis trees, and each branch of them represents a multi-camera track of a target that may move within a camera as well as move across cameras. Furthermore, multi-target tracking within a camera is performed simultaneously with the tree formation by manipulating a status of each track hypothesis. Each status represents three different stages of a multi-camera track: tracking, searching, and end-of-track. The tracking status means targets are tracked by a single camera tracker. In the searching status, the disappeared targets are examined if they reappear in other cameras. The end-of-track status does the target exited the camera network due to its lengthy invisibility. These three status assists MHT to form the track-hypothesis trees for multi-camera tracking. Furthermore, they present a gating technique for eliminating of unlikely observation-to-track association. In the experiments, they evaluate the proposed method using two datasets, DukeMTMC and NLPR_MCT, which demonstrates that the proposed method outperforms the state-of-the-art method in terms of improvement of the accuracy. In addition, they show that the proposed method can operate in real-time and online.

Toward an Optimal Solution for Multitarget Tracking

2007 IEEE International Conference on Image Processing, 2007

There are ever increasing number of applications of multitarget tracking and considerable research has been conducted to solve this problem. Multi-target tracking is a NP-hard problem and almost all of the present multi-target tracking algorithms are sub-optimal by finding the solution in a reduced hypothesis space. In this paper we introduce a new approach toward finding the optimal single frame solution for general multi-target tracking problem. Our proposed method finds the optimal solution using linear programming optimization method. The proposed method has been successfully applied to synthetic and real data.

Cooperative Multitarget Tracking With Efficient Split and Merge Handling

IEEE Transactions on Circuits and Systems for Video Technology, 2006

For applications such as behavior recognition it is important to maintain the identity of multiple targets, while tracking them in the presence of splits and merges, or occlusion of the targets by background obstacles. Here we propose an algorithm to handle multiple splits and merges of objects based on dynamic programming and a new geometric shape matching measure. We then cooperatively combine Kalman filter-based motion and shape tracking with the efficient and novel geometric shape matching algorithm. The system is fully automatic and requires no manual input of any kind for initialization of tracking. The target track initialization problem is formulated as computation of shortest paths in a directed and attributed graph using Dijkstra's shortest path algorithm. This scheme correctly initializes multiple target tracks for tracking even in the presence of clutter and segmentation errors which may occur in detecting a target. We present results on a large number of real world image sequences, where upto 17 objects have been tracked simultaneously in real-time, despite clutter, splits, and merges in measurements of objects. The complete tracking system including segmentation of moving objects works at 25 Hz on 352 288 pixel color image sequences on a 2.8-GHz Pentium-4 workstation.

Tracking and identification of multiple targets

In this paper, we study the problem of joint tracking and classification of several targets at the same time. Targets are considered to be known and sufficiently separated so that they cannot be confused. Our goal is to propose a full methodology that is robust to missing information. The classical probabilistic approach with Bayesian tools is improved with belief functions. A simulation concerning the identification of go fast boats in a piracy problem shows that our approach improves previous results.