On Spatial Attention in Object Tracking using a Synthetic Aperture Camera Array

2011 ◽  
Author(s):  
Christoph Walter ◽  
Maik Poggendorf ◽  
Norbert Elkmann ◽  
Felix Penzlin
Keyword(s):  
Object Tracking ◽  
Spatial Attention ◽  
Synthetic Aperture ◽  
Camera Array

On Spatial Attention in Object Tracking using a Synthetic Aperture Camera Array

2012 ◽  
Author(s):  
Christoph Walter ◽  
Maik Poggendorf ◽  
Norbert Elkmann ◽  
Felix Penzlin
Keyword(s):  
Object Tracking ◽  
Spatial Attention ◽  
Synthetic Aperture ◽  
Camera Array

scholarly journals Learning Soft Mask Based Feature Fusion with Channel and Spatial Attention for Robust Visual Object Tracking

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4021 ◽  
Author(s):  
Mustansar Fiaz ◽  
Arif Mahmood ◽  
Soon Ki Jung
Keyword(s):  
Object Tracking ◽  
Spatial Attention ◽  
Feature Fusion ◽  
State Of The Art ◽  
Feature Representation ◽  
Visual Object ◽  
Target Feature ◽  
Visual Object Tracking ◽  
Low Level ◽  
Benchmark Datasets

We propose to improve the visual object tracking by introducing a soft mask based low-level feature fusion technique. The proposed technique is further strengthened by integrating channel and spatial attention mechanisms. The proposed approach is integrated within a Siamese framework to demonstrate its effectiveness for visual object tracking. The proposed soft mask is used to give more importance to the target regions as compared to the other regions to enable effective target feature representation and to increase discriminative power. The low-level feature fusion improves the tracker robustness against distractors. The channel attention is used to identify more discriminative channels for better target representation. The spatial attention complements the soft mask based approach to better localize the target objects in challenging tracking scenarios. We evaluated our proposed approach over five publicly available benchmark datasets and performed extensive comparisons with 39 state-of-the-art tracking algorithms. The proposed tracker demonstrates excellent performance compared to the existing state-of-the-art trackers.

scholarly journals Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking

2020 ◽  
Vol 34 (04) ◽  
pp. 3684-3692
Author(s):  
Eric Crawford ◽  
Joelle Pineau
Keyword(s):  
Object Tracking ◽  
Spatial Attention ◽  
Intelligent Agent ◽  
Current Work ◽  
Visual World ◽  
Reasoning Process ◽  
Series Of Experiments ◽  
Spatial Invariance ◽  
Tracking Objects ◽  
Object Level

The ability to detect and track objects in the visual world is a crucial skill for any intelligent agent, as it is a necessary precursor to any object-level reasoning process. Moreover, it is important that agents learn to track objects without supervision (i.e. without access to annotated training videos) since this will allow agents to begin operating in new environments with minimal human assistance. The task of learning to discover and track objects in videos, which we call unsupervised object tracking, has grown in prominence in recent years; however, most architectures that address it still struggle to deal with large scenes containing many objects. In the current work, we propose an architecture that scales well to the large-scene, many-object setting by employing spatially invariant computations (convolutions and spatial attention) and representations (a spatially local object specification scheme). In a series of experiments, we demonstrate a number of attractive features of our architecture; most notably, that it outperforms competing methods at tracking objects in cluttered scenes with many objects, and that it can generalize well to videos that are larger and/or contain more objects than videos encountered during training.

scholarly journals Refocusing Algorithm Based on Nonlinear SAI System

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Bin Liu ◽  
Yue Luo ◽  
Yi-Hua Pan ◽  
Wen-Min Yan ◽  
Xin-Yu Zhang
Keyword(s):  
Light Field ◽  
Linear Array ◽  
Optical Field ◽  
Synthetic Aperture ◽  
Camera Array ◽  
Occluded Objects ◽  
Field Information ◽  
Different Depths ◽  
Camera Arrays ◽  
Partially Occluded

Synthetic aperture imaging (SAI) technology gets the light field information of the scene through the camera array. With the large virtual aperture, it can effectively acquire the information of the partially occluded object in the scene, and then we can focus on the arbitrary target plane corresponding to the reference perspective through the refocus algorithm. Meanwhile, objects that deviate from the plane will be blurred to varying degrees. However, when the object to be reconstructed in the scene is occluded by the complex foreground, the optical field information of the target cannot be effectively detected due to the limitation of the linear array. In order to deal with these problems, this paper proposes a nonlinear SAI method. This method can obtain the occluded object’s light field information reliably by using the nonlinear array. Experiments are designed for the nonlinear SAI, and refocusing is performed for the occluded objects with different camera arrays, different depths, and different distribution intervals. The results demonstrate that the method proposed in this paper is advanced than the traditional SAI method based on linear array.

scholarly journals Occluded-Object 3D Reconstruction Using Camera Array Synthetic Aperture Imaging

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 607 ◽  
Author(s):  
Zhao Pei ◽  
Yawen Li ◽  
Miao Ma ◽  
Jun Li ◽  
Chengcai Leng ◽  
...  
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Synthetic Aperture ◽  
Object Reconstruction ◽  
Synthetic Aperture Imaging ◽  
The Past ◽  
Camera Array ◽  
Occluded Objects ◽  
Great Progress ◽  
Novel Method

With the three-dimensional (3D) coordinates of objects captured by a sequence of images taken in different views, object reconstruction is a technique which aims to recover the shape and appearance information of objects. Although great progress in object reconstruction has been made over the past few years, object reconstruction in occlusion situations remains a challenging problem. In this paper, we propose a novel method to reconstruct occluded objects based on synthetic aperture imaging. Unlike most existing methods, which either assume that there is no occlusion in the scene or remove the occlusion from the reconstructed result, our method uses the characteristics of synthetic aperture imaging that can effectively reduce the influence of occlusion to reconstruct the scene with occlusion. The proposed method labels occlusion pixels according to variance and reconstructs the 3D point cloud based on synthetic aperture imaging. Accuracies of the point cloud are tested by calculating the spatial difference between occlusion and non-occlusion conditions. The experiment results show that the proposed method can handle the occluded situation well and demonstrates a promising performance.

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