Situation assessment: an end-to-end process for the detection of objects of interest

International audienceIn this article, semi-automatic approaches are developed for wide area situation assessment in near-real-time. The two-step method consists of two granularity levels. The first entity assessment uses a new multi-target tracking algorithm (hybridization of GM-CPHD filter and MHT with road constraints) on GMTI data. The situation is then assessed by detecting objects of interest such as convoys with other data types (SAR, video). These detections are based on Bayesian networks and their credibilistic counterpart

Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System

Neuromorphic vision sensor is a new passive sensing modality and a frameless sensor with a number of advantages over traditional cameras. Instead of wastefully sending entire images at fixed frame rate, neuromorphic vision sensor only transmits the local pixel-level changes caused by the movement in a scene"jats:italic" at the time they occur"/jats:italic". This results in advantageous characteristics, in terms of low energy consumption, high dynamic range, sparse event stream, and low response latency, which can be very useful in intelligent perception systems for modern intelligent transportation system (ITS) that requires efficient wireless data communication and low power embedded computing resources. In this paper, we propose the first neuromorphic vision based multivehicle detection and tracking system in ITS. The performance of the system is evaluated with a dataset recorded by a neuromorphic vision sensor mounted on a highway bridge. We performed a preliminary multivehicle tracking-by-clustering study using three classical clustering approaches and four tracking approaches. Our experiment results indicate that, by making full use of the low latency and sparse event stream, we could easily integrate an online tracking-by-clustering system running at a high frame rate, which far exceeds the real-time capabilities of traditional frame-based cameras. If the accuracy is prioritized, the tracking task can also be performed robustly at a relatively high rate with different combinations of algorithms. We also provide our dataset and evaluation approaches serving as the first neuromorphic benchmark in ITS and hopefully can motivate further research on neuromorphic vision sensors for ITS solutions. Document type: Articl

Tracking of Spline Modeled Extended Objects Using Random Finite Sets

In the field of autonomously driving vehicles the environment perception containing dynamic objects like other road users is essential. Especially, detecting other vehicles in the road traffic using sensor data is of utmost importance. As the sensor data and the applied system model for the objects of interest are noise corrupted, a filter algorithm must be used to track moving objects. Using LIDAR sensors one object gives rise to more than one measurement per time step and is therefore called extended object. This allows to jointly estimate the objects, position, as well as its orientation, extension and shape. Estimating an arbitrary shaped object comes with a higher computational effort than estimating the shape of an object that can be approximated using a basic geometrical shape like an ellipse or a rectangle. In the case of a vehicle, assuming a rectangular shape is an accurate assumption. A recently developed approach models the contour of a vehicle as periodic B-spline function. This representation is an easy to use tool, as the contour can be specified by some basis points in Cartesian coordinates. Also rotating, scaling and moving the contour is easy to handle using a spline contour. This contour model can be used to develop a measurement model for extended objects, that can be integrated into a tracking filter. Another approach modeling the shape of a vehicle is the so-called bounding box that represents the shape as rectangle. In this thesis the basics of single, multi and extended object tracking, as well as the basics of B-spline functions are addressed. Afterwards, the spline measurement model is established in detail and integrated into an extended Kalman filter to track a single extended object. An implementation of the resulting algorithm is compared with the rectangular shape estimator. The implementation of the rectangular shape estimator is provided. The comparison is done using long-term considerations with Monte Carlo simulations and by analyzing the results of a single run. Therefore, both algorithms are applied to the same measurements. The measurements are generated using an artificial LIDAR sensor in a simulation environment. In a real-world tracking scenario detecting several extended objects and measurements that do not originate from a real object, named clutter measurements, is possible. Also, the sudden appearance and disappearance of an object is possible. A filter framework investigated in recent years that can handle tracking multiple objects in a cluttered environment is a random finite set based approach. The idea of random finite sets and its use in a tracking filter is recapped in this thesis. Afterwards, the spline measurement model is included in a multi extended object tracking framework. An implementation of the resulting filter is investigated in a long-term consideration using Monte Carlo simulations and by analyzing the results of a single run. The multi extended object filter is also applied to artificial LIDAR measurements generated in a simulation environment. The results of comparing the spline based and rectangular based extended object trackers show a more stable performance of the spline extended object tracker. Also, some problems that have to be addressed in future works are discussed. The investigation of the resulting multi extended object tracker shows a successful integration of the spline measurement model in a multi extended object tracker. Also, with these results some problems remain, that have to be solved in future works

Space-based relative multitarget tracking

Access to space has expanded dramatically over the past decade. The growing popularity of small satellites, specifically cubesats, and the following launch initiatives have resulted in exponentially growing launch numbers into low Earth orbit. This growing congestion in space has punctuated the need for local space monitoring and autonomous satellite inspection. This work describes the development of a framework for monitoring local space and tracking multiple objects concurrently in a satellite\u27s neighborhood. The development of this multitarget tracking systems has produced collateral developments in numerical methods, relative orbital mechanics, and initial relative orbit determination. This work belongs to a class of navigation known as angles-only navigation, in which angles representing the direction to the target are measured but no range measurements are available. A key difference between this work and traditional angles-only relative navigation research is that angle measurements are collected from two separate cameras simultaneously. Such measurements, when coupled with the known location and orientation of the stereo cameras, can be used to resolve the relative range component of a target\u27s position. This fact is exploited to form initial statistical representations of the targets\u27 relative states, which are subsequently refined in Bayesian single-target and multitarget frameworks --Abstract, page iii

Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering

Monocular cameras are one of the most commonly used sensors in the automotive industry for autonomous vehicles. One major drawback using a monocular camera is that it only makes observations in the two dimensional image plane and can not directly measure the distance to objects. In this paper, we aim at filling this gap by developing a multi-object tracking algorithm that takes an image as input and produces trajectories of detected objects in a world coordinate system. We solve this by using a deep neural network trained to detect and estimate the distance to objects from a single input image. The detections from a sequence of images are fed in to a state-of-the art Poisson multi-Bernoulli mixture tracking filter. The combination of the learned detector and the PMBM filter results in an algorithm that achieves 3D tracking using only mono-camera images as input. The performance of the algorithm is evaluated both in 3D world coordinates, and 2D image coordinates, using the publicly available KITTI object tracking dataset. The algorithm shows the ability to accurately track objects, correctly handle data associations, even when there is a big overlap of the objects in the image, and is one of the top performing algorithms on the KITTI object tracking benchmark. Furthermore, the algorithm is efficient, running on average close to 20 frames per second.Comment: 8 pages, 2 figures, for associated videos, see https://goo.gl/Aoydg

Environment Modeling Based on Generic Infrastructure Sensor Interfaces Using a Centralized Labeled-Multi-Bernoulli Filter

Urban intersections put high demands on fully automated vehicles, in particular, if occlusion occurs. In order to resolve such and support vehicles in unclear situations, a popular approach is the utilization of additional information from infrastructure-based sensing systems. However, a widespread use of such systems is circumvented by their complexity and thus, high costs. Within this paper, a generic interface is proposed, which enables a huge variety of sensors to be connected. The sensors are only required to measure very few features of the objects, if multiple distributed sensors with different viewing directions are available. Furthermore, a Labeled Multi-Bernoulli (LMB) filter is presented, which can not only handle such measurements, but also infers missing object information about the objects' extents. The approach is evaluated on simulations and demonstrated on a real-world infrastructure setup

Multi Sensor Multi Target Perception and Tracking for Informed Decisions in Public Road Scenarios

Multi-target tracking in public traffic calls for a tracking system with automated track initiation and termination facilities in a randomly evolving driving environment. Besides, the key problem of data association needs to be handled effectively considering the limitations in the computational resources on-board an autonomous car. The challenge of the tracking problem is further evident in the use of high-resolution automotive sensors which return multiple detections per object. Furthermore, it is customary to use multiple sensors that cover different and/or over-lapping Field of View and fuse sensor detections to provide robust and reliable tracking. As a consequence, in high-resolution multi-sensor settings, the data association uncertainty, and the corresponding tracking complexity increases pointing to a systematic approach to handle and process sensor detections. In this work, we present a multi-target tracking system that addresses target birth/initiation and death/termination processes with automatic track management features. These tracking functionalities can help facilitate perception during common events in public traffic as participants (suddenly) change lanes, navigate intersections, overtake and/or brake in emergencies, etc. Various tracking approaches including the ones based on joint integrated probability data association (JIPDA) filter, Linear Multi-target Integrated Probabilistic Data Association (LMIPDA) Filter, and their multi-detection variants are adapted to specifically include algorithms that handle track initiation and termination, clutter density estimation and track management. The utility of the filtering module is further elaborated by integrating it into a trajectory tracking problem based on model predictive control. To cope with tracking complexity in the case of multiple high-resolution sensors, we propose a hybrid scheme that combines the approaches of data clustering at the local sensor and multiple detections tracking schemes at the fusion layer. We implement a track-to-track fusion scheme that de-correlates local (sensor) tracks to avoid double counting and apply a measurement partitioning scheme to re-purpose the LMIPDA tracking algorithm to multi-detection cases. In addition to the measurement partitioning approach, a joint extent and kinematic state estimation scheme are integrated into the LMIPDA approach to facilitate perception and tracking of an individual as well as group targets as applied to multi-lane public traffic. We formulate the tracking problem as a two hierarchical layer. This arrangement enhances the multi-target tracking performance in situations including but not limited to target initialization(birth process), target occlusion, missed detections, unresolved measurement, target maneuver, etc. Also, target groups expose complex individual target interactions to help in situation assessment which is challenging to capture otherwise. The simulation studies are complemented by experimental studies performed on single and multiple (group) targets. Target detections are collected from a high-resolution radar at a frequency of 20Hz; whereas RTK-GPS data is made available as ground truth for one of the target vehicle\u27s trajectory