You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking

Firstly, a new multi-object tracking framework is proposed in this paper based on multi-modal fusion. By integrating object detection and multi-object tracking into the same model, this framework avoids the complex data association process in the classical TBD paradigm, and requires no additional training. Secondly, confidence of historical trajectory regression is explored, possible states of a trajectory in the current frame (weak object or strong object) are analyzed and a confidence fusion module is designed to guide non-maximum suppression of trajectory and detection for ordered association. Finally, extensive experiments are conducted on the KITTI and Waymo datasets. The results show that the proposed method can achieve robust tracking by using only two modal detectors and it is more accurate than many of the latest TBD paradigm-based multi-modal tracking methods. The source codes of the proposed method are available at https://github.com/wangxiyang2022/YONTD-MOTComment: 10 pages, 9 figure

Conjugate priors for Bayesian object tracking

Object tracking refers to the problem of using noisy sensor measurements to determine the location and characteristics of objects of interest in clutter. Nowadays, object tracking has found applications in numerous research venues as well as application areas, including air traffic control, maritime navigation, remote sensing, intelligent video surveillance, and more recently environmental perception, which is a key enabling technology in autonomous vehicles. This thesis studies conjugate priors for Bayesian object tracking with focus on multi-object tracking (MOT) based on sets of trajectories. Finite Set Statistics provides an elegant Bayesian formulation of MOT in terms of the theory of random finite sets (RFSs). Conjugate priors are also of great interest as they provide families of distributions that are suitable to work with when seeking accurate approximations to the true posterior distributions. Many RFS-based MOT approaches are only concerned with multi-object filtering without attempting to estimate object trajectories. An appealing approach to building tracks is by computing the multi-object densities on sets of trajectories. This leads to the development of trajectory filters, e.g., filters based on Poisson multi-Bernoulli mixture (PMBM) conjugate priors.In this thesis, [Paper A] and [Paper B] consider the problem of point object tracking where an object generates at most one measurement per scan. In [Paper A], it is shown that the trajectory MBM filter is the solution to the MOT problem for standard point object models with multi-Bernoulli birth. In addition, the multi-scan implementations of trajectory PMBM and MBM filters are presented. In [Paper B], a solution for recovering full trajectory information, via the calculation of the posterior of the set of trajectories from a sequence of multi-object filtering densities and the multi-object dynamic model, is presented. [Paper C] and [Paper D] consider the problem of ex- tended object tracking where an object may generate multiple measurements per scan. In [Paper C], the extended object PMBM filter for sets of objects is generalized to sets of trajectories. In [Paper D], a learning-based extended ob- ject tracking algorithm using a hierarchical truncated Gaussian measurement model tailored for automotive radar measurements is presented

A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects

Recently, Minimum Cost Multicut Formulations have been proposed and proven to be successful in both motion trajectory segmentation and multi-target tracking scenarios. Both tasks benefit from decomposing a graphical model into an optimal number of connected components based on attractive and repulsive pairwise terms. The two tasks are formulated on different levels of granularity and, accordingly, leverage mostly local information for motion segmentation and mostly high-level information for multi-target tracking. In this paper we argue that point trajectories and their local relationships can contribute to the high-level task of multi-target tracking and also argue that high-level cues from object detection and tracking are helpful to solve motion segmentation. We propose a joint graphical model for point trajectories and object detections whose Multicuts are solutions to motion segmentation {\it and} multi-target tracking problems at once. Results on the FBMS59 motion segmentation benchmark as well as on pedestrian tracking sequences from the 2D MOT 2015 benchmark demonstrate the promise of this joint approach

Poisson Multi-Bernoulli Mixtures for Multiple Object Tracking

Multi-object tracking (MOT) refers to the process of estimating object trajectories of interest based on sequences of noisy sensor measurements obtained from multiple sources. Nowadays, MOT has found applications in numerous areas, including, e.g., air traffic control, maritime navigation, remote sensing, intelligent video surveillance, and more recently environmental perception, which is a key enabling technology in automated vehicles. This thesis studies Poisson multi-Bernoulli mixture (PMBM) conjugate priors for MOT. Finite Set Statistics provides an elegant Bayesian formulation of MOT based on random finite sets (RFSs), and a significant trend in RFSs-based MOT is the development of conjugate distributions in Bayesian probability theory, such as the PMBM distributions. Multi-object conjugate priors are of great interest as they provide families of distributions that are suitable to work with when seeking accurate approximations to the true posterior distributions. Many RFS-based MOT approaches are only concerned with multi-object filtering without attempting to estimate object trajectories. An appealing approach to building trajectories is by computing the multi-object densities on sets of trajectories. This leads to the development of many multi-object filters based on sets of trajectories, e.g., the trajectory PMBM filters. In this thesis, [Paper A] and [Paper B] consider the problem of point object tracking where an object generates at most one measurement per time scan. In [Paper A], a multi-scan implementation of trajectory PMBM filters via dual decomposition is presented. In [Paper B], a multi-trajectory particle smoother using backward simulation is presented for computing the multi-object posterior for sets of trajectories using a sequence of multi-object filtering densities and a multi-object dynamic model. [Paper C] and [Paper D] consider the problem of extended object tracking where an object may generate multiple measurements per time scan. In [Paper C], an extended object Poisson multi-Bernoulli (PMB) filter is presented, where the PMBM posterior density after the update step is approximated as a PMB. In [Paper D], a trajectory PMB filter for extended object tracking using belief propagation is presented, where the efficient PMB approximation is enabled by leveraging the PMBM conjugacy and the factor graph formulation

Multi-camera trajectory forecasting : pedestrian trajectory prediction in a network of cameras

We introduce the task of multi-camera trajectory forecasting (MCTF), where the future trajectory of an object is predicted in a network of cameras. Prior works consider forecasting trajectories in a single camera view. Our work is the first to consider the challenging scenario of forecasting across multiple non-overlapping camera views. This has wide applicability in tasks such as re-identification and multi-target multi-camera tracking. To facilitate research in this new area, we release the Warwick-NTU Multi-camera Forecasting Database (WNMF), a unique dataset of multi-camera pedestrian trajectories from a network of 15 synchronized cameras. To accurately label this large dataset (600 hours of video footage), we also develop a semi-automated annotation method. An effective MCTF model should proactively anticipate where and when a person will re-appear in the camera network. In this paper, we consider the task of predicting the next camera a pedestrian will re-appear after leaving the view of another camera, and present several baseline approaches for this. The labeled database is available online https://github.com/olly-styles/Multi-Camera-Trajectory-Forecastin

Reinforcement learning control for coordinated manipulation of multi-robots

In this paper, coordination control is investigated for multi-robots to manipulate an object with a common desired trajectory. Both trajectory tracking and control input minimization are considered for each individual robot manipulator, such that possible disagreement between different manipulators can be handled. Reinforcement learning is employed to cope with the problem of unknown dynamics of both robots and the manipulated object. It is rigorously proven that the proposed method guarantees the coordination control of the multi-robots system under study. The validity of the proposed method is verified through simulation studies

A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects

Recently, Minimum Cost Multicut Formulations have been proposed and proven to be successful in both motion trajectory segmentation and multi-target tracking scenarios. Both tasks benefit from decomposing a graphical model into an optimal number of connected components based on attractive and repulsive pairwise terms. The two tasks are formulated on different levels of granularity and, accordingly, leverage mostly local information for motion segmentation and mostly high-level information for multi-target tracking. In this paper we argue that point trajectories and their local relationships can contribute to the high-level task of multi-target tracking and also argue that high-level cues from object detection and tracking are helpful to solve motion segmentation. We propose a joint graphical model for point trajectories and object detections whose Multicuts are solutions to motion segmentation {\it and} multi-target tracking problems at once. Results on the FBMS59 motion segmentation benchmark as well as on pedestrian tracking sequences from the 2D MOT 2015 benchmark demonstrate the promise of this joint approach