Event Stream-based Visual Object Tracking: HDETrack V2 and A High-Definition Benchmark

• URL: http://arxiv.org/abs/2502.05574v1
• Date: Sat, 08 Feb 2025 13:59:52 GMT
• Title: Event Stream-based Visual Object Tracking: HDETrack V2 and A High-Definition Benchmark
• Authors: Shiao Wang, Xiao Wang, Chao Wang, Liye Jin, Lin Zhu, Bo Jiang, Yonghong Tian, Jin Tang,
• Abstract summary: We introduce a novel hierarchical knowledge distillation strategy to guide the learning of the student Transformer network. We adapt the network model to specific target objects during testing via a newly proposed test-time tuning strategy. We propose EventVOT, the first large-scale high-resolution event-based tracking dataset.
• Score: 36.9654606035663
• License:
• Abstract: We then introduce a novel hierarchical knowledge distillation strategy that incorporates the similarity matrix, feature representation, and response map-based distillation to guide the learning of the student Transformer network. We also enhance the model's ability to capture temporal dependencies by applying the temporal Fourier transform to establish temporal relationships between video frames. We adapt the network model to specific target objects during testing via a newly proposed test-time tuning strategy to achieve high performance and flexibility in target tracking. Recognizing the limitations of existing event-based tracking datasets, which are predominantly low-resolution, we propose EventVOT, the first large-scale high-resolution event-based tracking dataset. It comprises 1141 videos spanning diverse categories such as pedestrians, vehicles, UAVs, ping pong, etc. Extensive experiments on both low-resolution (FE240hz, VisEvent, FELT), and our newly proposed high-resolution EventVOT dataset fully validated the effectiveness of our proposed method. Both the benchmark dataset and source code have been released on https://github.com/Event-AHU/EventVOT_Benchmark

Related papers

• SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features [52.213656737672935]
  SpikeMOT is an event-based multi-object tracker. SpikeMOT uses spiking neural networks to extract sparsetemporal features from event streams associated with objects.
  arXiv  Detail & Related papers  (2023-09-29T05:13:43Z)
• Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline [38.42400442371156]
  Existing works either utilize aligned RGB and event data for accurate tracking or directly learn an event-based tracker. We propose a novel hierarchical knowledge distillation framework that can fully utilize multi-modal / multi-view information during training to facilitate knowledge transfer. We propose the first large-scale high-resolution ($1280 times 720$) dataset named EventVOT. It contains 1141 videos and covers a wide range of categories such as pedestrians, vehicles, UAVs, ping pongs, etc.
  arXiv  Detail & Related papers  (2023-09-26T01:42:26Z)
• TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement [64.11385310305612]
  We present a novel model for Tracking Any Point (TAP) that effectively tracks any queried point on any physical surface throughout a video sequence. Our approach employs two stages: (1) a matching stage, which independently locates a suitable candidate point match for the query point on every other frame, and (2) a refinement stage, which updates both the trajectory and query features based on local correlations. The resulting model surpasses all baseline methods by a significant margin on the TAP-Vid benchmark, as demonstrated by an approximate 20% absolute average Jaccard (AJ) improvement on DAVIS.
  arXiv  Detail & Related papers  (2023-06-14T17:07:51Z)
• Event Voxel Set Transformer for Spatiotemporal Representation Learning on Event Streams [19.957857885844838]
  Event cameras are neuromorphic vision sensors that record a scene as sparse and asynchronous event streams. We propose an attentionaware model named Event Voxel Set Transformer (EVSTr) for efficient representation learning on event streams. Experiments show that EVSTr achieves state-of-the-art performance while maintaining low model complexity.
  arXiv  Detail & Related papers  (2023-03-07T12:48:02Z)
• Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric [53.88188265943762]
  We propose a single-stage backbone network for Color-Event Unified Tracking (CEUTrack), which achieves the above functions simultaneously. Our proposed CEUTrack is simple, effective, and efficient, which achieves over 75 FPS and new SOTA performance.
  arXiv  Detail & Related papers  (2022-11-20T16:01:31Z)
• Weakly Supervised Video Salient Object Detection [79.51227350937721]
  We present the first weakly supervised video salient object detection model based on relabeled "fixation guided scribble annotations" An "Appearance-motion fusion module" and bidirectional ConvLSTM based framework are proposed to achieve effective multi-modal learning and long-term temporal context modeling.
  arXiv  Detail & Related papers  (2021-04-06T09:48:38Z)
• TDIOT: Target-driven Inference for Deep Video Object Tracking [0.2457872341625575]
  In this work, we adopt the pre-trained Mask R-CNN deep object detector as the baseline. We introduce a novel inference architecture placed on top of FPN-ResNet101 backbone of Mask R-CNN to jointly perform detection and tracking. The proposed single object tracker, TDIOT, applies an appearance similarity-based temporal matching for data association.
  arXiv  Detail & Related papers  (2021-03-19T20:45:06Z)
• Fast Video Object Segmentation With Temporal Aggregation Network and Dynamic Template Matching [67.02962970820505]
  We introduce "tracking-by-detection" into Video Object (VOS) We propose a new temporal aggregation network and a novel dynamic time-evolving template matching mechanism to achieve significantly improved performance. We achieve new state-of-the-art performance on the DAVIS benchmark without complicated bells and whistles in both speed and accuracy, with a speed of 0.14 second per frame and J&F measure of 75.9% respectively.
  arXiv  Detail & Related papers  (2020-07-11T05:44:16Z)
• Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking [87.0297771292994]
  We propose an Event-based Tracking-by-Detection (ETD) method for generic bounding box-based object tracking. To achieve this goal, we present an Adaptive Time-Surface with Linear Time Decay (ATSLTD) event-to-frame conversion algorithm. We compare the proposed ETD method with seven popular object tracking methods, that are based on conventional cameras or event cameras, and two variants of ETD.
  arXiv  Detail & Related papers  (2020-02-13T15:58:31Z)

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.