A fast multi-object tracking system using an object detector ensemble

Multiple-Object Tracking (MOT) is of crucial importance for applications such as retail video analytics and video surveillance. Object detectors are often the computational bottleneck of modern MOT systems, limiting their use for real-time applications. In this paper, we address this issue by leveraging on an ensemble of detectors, each running every f frames. We measured the performance of our system in the MOT16 benchmark. The proposed model surpassed other online entries of the MOT16 challenge in speed, while maintaining an acceptable accuracy.Comment: 5 pages, 4 figures, 1 table, published in 2019 IEEE Colombian Conference on Applications in Computational Intelligence (ColCACI

Multi-Object Tracking System based on LiDAR and RADAR for Intelligent Vehicles applications

El presente Trabajo Fin de Grado tiene como objetivo el desarrollo de un Sistema de Detección y Multi-Object Tracking 3D basado en la fusión sensorial de LiDAR y RADAR para aplicaciones de conducción autónoma basándose en algoritmos tradicionales de Machine Learning. La implementación realizada está basada en Python, ROS y cumple requerimientos de tiempo real. En la etapa de detección de objetos se utiliza el algoritmo de segmentación del plano RANSAC, para una posterior extracción de Bounding Boxes mediante DBSCAN. Una Late Sensor Fusion mediante Intersection over Union 3D y un sistema de tracking BEV-SORT completan la arquitectura propuesta.This Final Degree Project aims to develop a 3D Multi-Object Tracking and Detection System based on the Sensor Fusion of LiDAR and RADAR for autonomous driving applications based on traditional Machine Learning algorithms. The implementation is based on Python, ROS and complies with real-time requirements. In the Object Detection stage, the RANSAC plane segmentation algorithm is used, for a subsequent extraction of Bounding Boxes using DBSCAN. A Late Sensor Fusion using Intersection over Union 3D and a BEV-SORT tracking system complete the proposed architecture.Grado en Ingeniería en Electrónica y Automática Industria

Multi-Object Tracking with Interacting Vehicles and Road Map Information

In many applications, tracking of multiple objects is crucial for a perception of the current environment. Most of the present multi-object tracking algorithms assume that objects move independently regarding other dynamic objects as well as the static environment. Since in many traffic situations objects interact with each other and in addition there are restrictions due to drivable areas, the assumption of an independent object motion is not fulfilled. This paper proposes an approach adapting a multi-object tracking system to model interaction between vehicles, and the current road geometry. Therefore, the prediction step of a Labeled Multi-Bernoulli filter is extended to facilitate modeling interaction between objects using the Intelligent Driver Model. Furthermore, to consider road map information, an approximation of a highly precise road map is used. The results show that in scenarios where the assumption of a standard motion model is violated, the tracking system adapted with the proposed method achieves higher accuracy and robustness in its track estimations

Real-Time Bird's Eye View Multi-Object Tracking system based on Fast Encoders for object detection

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), September 20-23, 2020, Rhodes, Greece. Virtual Conference.This paper presents a Real-Time Bird’s Eye View Multi Object Tracking (MOT) system pipeline for an Autonomous Electric car, based on Fast Encoders for object detection and a combination of Hungarian algorithm and Bird’s Eye View (BEV) Kalman Filter, respectively used for data association and state estimation. The system is able to analyze 360 degrees around the ego-vehicle as well as estimate the future trajectories of the environment objects, being the essential input for other layers of a self-driving architecture, such as the control or decision-making. First, our system pipeline is described, merging the concepts of online and realtime DATMO (Deteccion and Tracking of Multiple Objects), ROS (Robot Operating System) and Docker to enhance the integration of the proposed MOT system in fully-autonomous driving architectures. Second, the system pipeline is validated using the recently proposed KITTI-3DMOT evaluation tool that demonstrates the full strength of 3D localization and tracking of a MOT system. Finally, a comparison of our proposal with other state-of-the-art approaches is carried out in terms of performance by using the mainstream metrics used on MOT benchmarks and the recently proposed integral MOT metrics, evaluating the performance of the tracking system over all detection thresholds.Ministerio de Ciencia, Innovación y UniversidadesComunidad de Madri