Tracking by Joint Local and Global Search: A Target-aware Attention
based Approach
- URL: http://arxiv.org/abs/2106.04840v1
- Date: Wed, 9 Jun 2021 06:54:15 GMT
- Title: Tracking by Joint Local and Global Search: A Target-aware Attention
based Approach
- Authors: Xiao Wang, Jin Tang, Bin Luo, Yaowei Wang, Yonghong Tian, Feng Wu
- Abstract summary: We propose a novel target-aware attention mechanism (termed TANet) to conduct joint local and global search for robust tracking.
Specifically, we extract the features of target object patch and continuous video frames, then we track and feed them into a decoder network to generate target-aware global attention maps.
In the tracking procedure, we integrate the target-aware attention with multiple trackers by exploring candidate search regions for robust tracking.
- Score: 63.50045332644818
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Tracking-by-detection is a very popular framework for single object tracking
which attempts to search the target object within a local search window for
each frame. Although such local search mechanism works well on simple videos,
however, it makes the trackers sensitive to extremely challenging scenarios,
such as heavy occlusion and fast motion. In this paper, we propose a novel and
general target-aware attention mechanism (termed TANet) and integrate it with
tracking-by-detection framework to conduct joint local and global search for
robust tracking. Specifically, we extract the features of target object patch
and continuous video frames, then we concatenate and feed them into a decoder
network to generate target-aware global attention maps. More importantly, we
resort to adversarial training for better attention prediction. The appearance
and motion discriminator networks are designed to ensure its consistency in
spatial and temporal views. In the tracking procedure, we integrate the
target-aware attention with multiple trackers by exploring candidate search
regions for robust tracking. Extensive experiments on both short-term and
long-term tracking benchmark datasets all validated the effectiveness of our
algorithm. The project page of this paper can be found at
\url{https://sites.google.com/view/globalattentiontracking/home/extend}.
Related papers
- DenseTrack: Drone-based Crowd Tracking via Density-aware Motion-appearance Synergy [33.57923199717605]
Drone-based crowd tracking faces difficulties in accurately identifying and monitoring objects from an aerial perspective.
To address these challenges, we present the Density-aware Tracking (DenseTrack) framework.
DenseTrack capitalizes on crowd counting to precisely determine object locations, blending visual and motion cues to improve the tracking of small-scale objects.
arXiv Detail & Related papers (2024-07-24T13:39:07Z) - Single Object Tracking Research: A Survey [44.24280758718638]
This paper presents the rationale and works of two most popular tracking frameworks in past ten years.
We present some deep learning based tracking methods categorized by different network structures.
We also introduce some classical strategies for handling the challenges in tracking problem.
arXiv Detail & Related papers (2022-04-25T02:59:15Z) - Global Tracking via Ensemble of Local Trackers [14.010150696810316]
Existing long-term tracking methods follow two typical strategies.
We combine the advantages of both strategies: tracking the target in a global view while exploiting the temporal context.
Our method performs favorably against state-of-the-art algorithms.
arXiv Detail & Related papers (2022-03-30T06:44:47Z) - Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT
Philosophy [63.91005999481061]
A practical long-term tracker typically contains three key properties, i.e. an efficient model design, an effective global re-detection strategy and a robust distractor awareness mechanism.
We propose a two-task tracking frame work (named DMTrack) to achieve distractor-aware fast tracking via Dynamic convolutions (d-convs) and Multiple object tracking (MOT) philosophy.
Our tracker achieves state-of-the-art performance on the LaSOT, OxUvA, TLP, VOT2018LT and VOT 2019LT benchmarks and runs in real-time (3x faster
arXiv Detail & Related papers (2021-04-25T00:59:53Z) - Dynamic Attention guided Multi-Trajectory Analysis for Single Object
Tracking [62.13213518417047]
We propose to introduce more dynamics by devising a dynamic attention-guided multi-trajectory tracking strategy.
In particular, we construct dynamic appearance model that contains multiple target templates, each of which provides its own attention for locating the target in the new frame.
After spanning the whole sequence, we introduce a multi-trajectory selection network to find the best trajectory that delivers improved tracking performance.
arXiv Detail & Related papers (2021-03-30T05:36:31Z) - Learning to Track with Object Permanence [61.36492084090744]
We introduce an end-to-end trainable approach for joint object detection and tracking.
Our model, trained jointly on synthetic and real data, outperforms the state of the art on KITTI, and MOT17 datasets.
arXiv Detail & Related papers (2021-03-26T04:43:04Z) - Graph Attention Tracking [76.19829750144564]
We propose a simple target-aware Siamese graph attention network for general object tracking.
Experiments on challenging benchmarks including GOT-10k, UAV123, OTB-100 and LaSOT demonstrate that the proposed SiamGAT outperforms many state-of-the-art trackers.
arXiv Detail & Related papers (2020-11-23T04:26:45Z) - Tracking-by-Counting: Using Network Flows on Crowd Density Maps for
Tracking Multiple Targets [96.98888948518815]
State-of-the-art multi-object tracking(MOT) methods follow the tracking-by-detection paradigm.
We propose a new MOT paradigm, tracking-by-counting, tailored for crowded scenes.
arXiv Detail & Related papers (2020-07-18T19:51:53Z)
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.