3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation
3D object detection using LiDAR data remains a key task for applications like autonomous driving and robotics. Unlike in the case of 2D images, LiDAR data is almost always collected over a period of time. However, most work in this area has focused on performing detection independent of the temporal domain. In this paper we present 3D-FCT, a Siamese network architecture that utilizes temporal information to simultaneously perform the related tasks of 3D object detection and tracking. The network is trained to predict the movement of an object based on the correlation features of extracted keypoints across time. Calculating correlation across keypoints only allows for real-time object detection. We further extend the multi-task objective to include a tracking regression loss. Finally, we produce high accuracy detections by linking short-term object tracklets into long term tracks based on the predicted tracks. Our proposed method is evaluated on the KITTI tracking dataset where it is shown to provide an improvement of 5.57% mAP over a state-of-the-art approach.
Code (0)
등록된 구현이 없습니다.
Tasks
3D Object Detection3D Object TrackingAutonomous DrivingFeature CorrelationObjectobject-detectionObject DetectionReal-Time Object DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Joint Counting, Detection and Re-Identification for Multi-Object Tracking
The recent trend in 2D multiple object tracking (MOT) is jointly solving detection and tracking, where object detection and appearance feature (or motion) are learned simultaneously. Despite competitive performance, in c…
Multi-Object TrackingMultiple Object TrackingObjectobject-detection+2Detect to Track and Track to Detect
Recent approaches for high accuracy detection and tracking of object categories in video consist of complex multistage solutions that become more cumbersome each year. In this paper we propose a ConvNet architecture that…
Objectobject-detectionObject DetectionGlobal Correlation Network: End-to-End Joint Multi-Object Detection and Tracking
Multi-object tracking (MOT) has made great progress in recent years, but there are still some problems. Most MOT algorithms follow tracking-by-detection framework, which separates detection and tracking into two independ…
ManagementMulti-Object TrackingObjectobject-detection+2Correlation-Aware Deep Tracking
Robustness and discrimination power are two fundamental requirements in visual object tracking. In most tracking paradigms, we find that the features extracted by the popular Siamese-like networks cannot fully discrimina…
Feature CorrelationObject TrackingVisual Object TrackingMultiple Object Tracking with Correlation Learning
Recent works have shown that convolutional networks have substantially improved the performance of multiple object tracking by simultaneously learning detection and appearance features. However, due to the local percepti…
Multiple Object TrackingObjectObject TrackingSelf-Supervised Learning