Dim Small Target Detection and Tracking: A Novel Method Based on Temporal Energy Selective Scaling and Trajectory Association

• URL: http://arxiv.org/abs/2405.09054v1
• Date: Wed, 15 May 2024 03:02:21 GMT
• Title: Dim Small Target Detection and Tracking: A Novel Method Based on Temporal Energy Selective Scaling and Trajectory Association
• Authors: Weihua Gao, Wenlong Niu, Wenlong Lu, Pengcheng Wang, Zhaoyuan Qi, Xiaodong Peng, Zhen Yang,
• Abstract summary: In this article, we analyze the difficulty based on spatial features and the feasibility based on temporal features of realizing effective detection. According to this analysis, we use a multi-frame as a detection unit and propose a detection method based on temporal energy selective scaling (TESS) For the target-present pixel, the target passing through the pixel will bring a weak transient disturbance on the intensity temporal profiles (ITPs) formed by pixels. We use a well-designed function to amplify the transient disturbance, suppress the background and noise components, and output the trajectory of the target on the multi-frame detection unit
• Score: 8.269449428849867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The detection and tracking of small targets in passive optical remote sensing (PORS) has broad applications. However, most of the previously proposed methods seldom utilize the abundant temporal features formed by target motion, resulting in poor detection and tracking performance for low signal-to-clutter ratio (SCR) targets. In this article, we analyze the difficulty based on spatial features and the feasibility based on temporal features of realizing effective detection. According to this analysis, we use a multi-frame as a detection unit and propose a detection method based on temporal energy selective scaling (TESS). Specifically, we investigated the composition of intensity temporal profiles (ITPs) formed by pixels on a multi-frame detection unit. For the target-present pixel, the target passing through the pixel will bring a weak transient disturbance on the ITP and introduce a change in the statistical properties of ITP. We use a well-designed function to amplify the transient disturbance, suppress the background and noise components, and output the trajectory of the target on the multi-frame detection unit. Subsequently, to solve the contradiction between the detection rate and the false alarm rate brought by the traditional threshold segmentation, we associate the temporal and spatial features of the output trajectory and propose a trajectory extraction method based on the 3D Hough transform. Finally, we model the trajectory of the target and propose a trajectory-based multi-target tracking method. Compared with the various state-of-the-art detection and tracking methods, experiments in multiple scenarios prove the superiority of our proposed methods.

Related papers

• Sparse Prior Is Not All You Need: When Differential Directionality Meets Saliency Coherence for Infrared Small Target Detection [15.605122893098981]
  This study introduces a Sparse Differential Directionality prior (SDD) framework. We leverage the distinct directional characteristics of targets to differentiate them from the background. We further enhance target detectability with a saliency coherence strategy. A Proximal Alternating Minimization-based (PAM) algorithm efficiently solves our proposed model.
  arXiv  Detail & Related papers  (2024-07-22T04:32:43Z)
• Efficient Meta-Learning Enabled Lightweight Multiscale Few-Shot Object Detection in Remote Sensing Images [15.12889076965307]
  YOLOv7 one-stage detector is subjected to a novel meta-learning training framework. This transformation allows the detector to adeptly address FSOD tasks while capitalizing on its inherent advantage of lightweight. To validate the effectiveness of our proposed detector, we conducted performance comparisons with current state-of-the-art detectors.
  arXiv  Detail & Related papers  (2024-04-29T04:56:52Z)
• Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving [69.20604395205248]
  We present a new 3D point-based detector model, named Shift-SSD, for precise 3D object detection in autonomous driving. We introduce an intriguing Cross-Cluster Shifting operation to unleash the representation capacity of the point-based detector. We conduct extensive experiments on the KITTI, runtime, and nuScenes datasets, and the results demonstrate the state-of-the-art performance of Shift-SSD.
  arXiv  Detail & Related papers  (2024-03-10T10:36:32Z)
• An Adaptive Spatial-Temporal Local Feature Difference Method for Infrared Small-moving Target Detection [8.466660143185493]
  We propose a novel method called spatial-temporal local feature difference (STLFD) with adaptive background suppression (ABS) Our approach utilizes filters in the spatial and temporal domains and performs pixel-level ABS on the output to enhance the contrast between the target and the background. Our experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods for infrared small-moving target detection.
  arXiv  Detail & Related papers  (2023-09-05T08:56:20Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Detecting changes to sub-diffraction objects with quantum-optimal speed and accuracy [0.8409980020848168]
  We evaluate the best possible average latency, for a fixed false alarm rate, for sub-diffraction incoherent imaging. We find that direct focal-plane detection of the incident optical intensity achieves sub-optimal detection latencies. We verify these results via Monte Carlo simulation of the change detection procedure and quantify a growing gap between the conventional and quantum-optimal receivers.
  arXiv  Detail & Related papers  (2023-08-14T16:48:18Z)
• Spatio-Temporal Point Process for Multiple Object Tracking [30.041104276095624]
  Multiple Object Tracking (MOT) focuses on modeling the relationship of detected objects among consecutive frames and merge them into different trajectories. We propose a novel framework that can effectively predict and mask-out noisy and confusing detection results before associating objects into trajectories.
  arXiv  Detail & Related papers  (2023-02-05T18:14:08Z)
• Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization [81.29406957201458]
  Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects. We argue that such a mechanism has fundamental limitations in building an effective regression loss for rotation detection. We propose to model the rotated objects as Gaussian distributions. We extend our approach from 2-D to 3-D with a tailored algorithm design to handle the heading estimation.
  arXiv  Detail & Related papers  (2022-09-22T07:50:48Z)
• Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions [65.84090965167535]
  We present Neural Motion Fields, a novel object representation which encodes both object point clouds and the relative task trajectories as an implicit value function parameterized by a neural network. This object-centric representation models a continuous distribution over the SE(3) space and allows us to perform grasping reactively by leveraging sampling-based MPC to optimize this value function.
  arXiv  Detail & Related papers  (2022-06-29T18:47:05Z)
• Implicit Motion Handling for Video Camouflaged Object Detection [60.98467179649398]
  We propose a new video camouflaged object detection (VCOD) framework. It can exploit both short-term and long-term temporal consistency to detect camouflaged objects from video frames.
  arXiv  Detail & Related papers  (2022-03-14T17:55:41Z)
• FMODetect: Robust Detection and Trajectory Estimation of Fast Moving Objects [110.29738581961955]
  We propose the first learning-based approach for detection and trajectory estimation of fast moving objects. The proposed method first detects all fast moving objects as a truncated distance function to the trajectory. For the sharp appearance estimation, we propose an energy minimization based deblurring.
  arXiv  Detail & Related papers  (2020-12-15T11:05:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.