High speed long-term visual object tracking algorithm for real robot systems

Jiang, Muxi; Li, Rui; Liu, Qisheng; Shi, Yahui; Tlelo-Cuautle, Esteban

doi:10.1016/j.neucom.2020.12.113

Cited by 20 publications

(4 citation statements)

References 19 publications

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“…Correlation filters are popular due to high tracking accuracy and high computational efficiency. The correlation filter by Bolme et al [40] has been extended to many variants such as kernel correlation filters [41,42], long-term memory [43][44][45], multi-dimensional features [46,47], part-based strategies [48][49][50], scale es-timation [51][52][53], context-aware filters [54][55][56], spatial-temporal regularization [57][58][59], deep learning [60,61], and multi-feature fusion [62][63][64]. Existing correlation filter-based trackers generally follow ridge regression models.…”

Section: Correlation Filter Trackingmentioning

confidence: 99%

Object Tracking in Hyperspectral-Oriented Video with Fast Spatial-Spectral Features

et al. 2021

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This paper presents a correlation filter object tracker based on fast spatial-spectral features (FSSF) to realize robust, real-time object tracking in hyperspectral surveillance video. Traditional object tracking in surveillance video based only on appearance information often fails in the presence of background clutter, low resolution, and appearance changes. Hyperspectral imaging uses unique spectral properties as well as spatial information to improve tracking accuracy in such challenging environments. However, the high-dimensionality of hyperspectral images causes high computational costs and difficulties for discriminative feature extraction. In FSSF, the real-time spatial-spectral convolution (RSSC) kernel is updated in real time in the Fourier transform domain without offline training to quickly extract discriminative spatial-spectral features. The spatial-spectral features are integrated into correlation filters to complete the hyperspectral tracking. To validate the proposed scheme, we collected a hyperspectral surveillance video (HSSV) dataset consisting of 70 sequences in 25 bands. Extensive experiments confirm the advantages and the efficiency of the proposed FSSF for object tracking in hyperspectral video tracking in challenging conditions of background clutter, low resolution, and appearance changes.

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Section: Correlation Filter Trackingmentioning

confidence: 99%

Object Tracking in Hyperspectral-Oriented Video with Fast Spatial-Spectral Features

et al. 2021

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“…However, as the network depth of these models continues to grow, the storage space required for the models and the computational resources needed for inference also increase, posing challenges for specific tasks that cannot utilize high-performance processors. Consequently, many researchers have begun to investigate how to ensure that models perform well on edge devices with limited computational resources [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Pruning and Distillation Based Compression Method for Sonar Image Detection Models

Cheng,

Hou,

Wang

et al. 2024

JMSE

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Accurate underwater target detection is crucial for the operation of autonomous underwater vehicles (AUVs), enhancing their environmental awareness and target search and rescue capabilities. Current deep learning-based detection models are typically large, requiring substantial storage and computational resources. However, the limited space on AUVs poses significant challenges for deploying these models on the embedded processors. Therefore, research on model compression is of great practical importance, aiming to reduce model parameters and computational load without significantly sacrificing accuracy. To address the challenge of deploying large detection models, this paper introduces an automated pruning method based on dependency graphs and successfully implements efficient pruning on the YOLOv7 model. To mitigate the accuracy degradation caused by extensive pruning, we design a hybrid distillation method that combines output-based and feature-based distillation techniques, thereby improving the detection accuracy of the pruned model. Finally, we deploy the compressed model on an embedded processor within an AUV to evaluate its performance. Multiple experiments confirm the effectiveness of our proposed method in practical applications.

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“…Single object tracking (SOT) has become a significant issue of computer vision, which contributes widely to various fields, such as autonomous driving [15,18], security surveillance [26,30] and robotics [2,11], etc. With the fast development of 3D sensors, such as LiDAR, 3D data reflecting real-world coordinates and object sizes can be captured.…”

Section: Introductionmentioning

confidence: 99%

Object Preserving Siamese Network for Single Object Tracking on Point Clouds

Zhao¹,

Zhao²,

Wang³

et al. 2023

Preprint

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Obviously, the object is the key factor of the 3D single object tracking (SOT) task. However, previous Siamesebased trackers overlook the negative effects brought by randomly dropped object points during backbone sampling, which hinder trackers to predict accurate bounding boxes (BBoxes). Exploring an approach that seeks to maximize the preservation of object points and their object-aware features is of particular significance. Motivated by this, we propose an Object Preserving Siamese Network (OP-SNet), which can significantly maintain object integrity and boost tracking performance. Firstly, the object highlighting module enhances the object-aware features and extracts discriminative features from template and search area. Then, the object-preserved sampling selects object candidates to obtain object-preserved search area seeds and drop the background points that contribute less to tracking. Finally, the object localization network precisely locates 3D BBoxes based on the object-preserved search area seeds. Extensive experiments demonstrate our method outperforms the state-of-the-art performance (∼9.4% and ∼2.5% success gain on KITTI and Waymo Open Dataset respectively).

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High speed long-term visual object tracking algorithm for real robot systems

Cited by 20 publications

References 19 publications

Object Tracking in Hyperspectral-Oriented Video with Fast Spatial-Spectral Features

Object Tracking in Hyperspectral-Oriented Video with Fast Spatial-Spectral Features

A Pruning and Distillation Based Compression Method for Sonar Image Detection Models

Object Preserving Siamese Network for Single Object Tracking on Point Clouds

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