Recently, Siamese-based trackers have achieved excellent performance in object tracking. However, the high speed and deformation of objects movement process make tracking difficult. Therefore, we incorporated cascaded region-proposal-network (RPN) fusion coordinate attention into Siamese trackers. The proposed network framework consists three parts: a feature-extraction sub-network, block, RPN ...

Abstract In consideration of the complementary characteristics between visible light and infrared images, this paper proposes a novel method for object detection based on fusion these two types thereby enhancing accuracy even under harsh environmental conditions. Specifically, we employ an improved AE network, which encodes decodes images into dual-scale image decomposition. By reconstructing o...

Accurate and reliable perception systems are essential for autonomous driving robotics. To achieve this, 3D object detection with multi-sensors is necessary. Existing detectors have significantly improved accuracy by adopting a two-stage paradigm that relies solely on LiDAR point clouds proposal refinement. However, the sparsity of clouds, particularly faraway points, makes it difficult LiDAR-o...

Sensor data fusion is essential for environmental perception within smart traffic applications. By using multiple sensors cooperatively, the accuracy and probability of are increased, which crucial critical scenarios or under bad weather conditions. In this paper, a modular real-time capable multi-sensor framework presented tested to fuse on object list level from distributed automotive (camera...

Indoor object detection and tracking using millimeter-wave (mmWave) radar sensors have received much attention recently due to the emergence of applications energy assignment, privacy, health, safety. Increasing valid field view system accuracy through multi-sensors is critical achieving an efficient system. This paper uses two mmWave for accurate tracking: noise reduction stages reduce disting...