1. Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan 2. Department of Computer Science, Bahria University, Lahore 54600, Pakistan 3. Department of Industrial Security, Chung-Ang University, Seoul 06974, Republic of Korea 4. Department of Computer Engineering, Mokwon University, Daejeon 35349, Republic of Korea
Innovations on the Internet of Everything (IoE) enabled systems are driving a change in the settings where we interact in smart units, recognized globally as smart city environments. However, intelligent video-surveillance systems are critical to increasing the security of these smart cities. More precisely, in today’s world of smart video surveillance, person re-identification (Re-ID) has gained increased consideration by researchers. Various researchers have designed deep learning-based algorithms for person Re-ID because they have achieved substantial breakthroughs in computer vision problems. In this line of research, we designed an adaptive feature refinement-based deep learning architecture to conduct person Re-ID. In the proposed architecture, the inter-channel and inter-spatial relationship of features between the images of the same individual taken from nonidentical camera viewpoints are focused on learning spatial and channel attention. In addition, the spatial pyramid pooling layer is inserted to extract the multiscale and fixed-dimension feature vectors irrespective of the size of the feature maps. Furthermore, the model’s effectiveness is validated on the CUHK01 and CUHK02 datasets. When compared with existing approaches, the approach presented in this paper achieves encouraging Rank 1 and 5 scores of 24.6% and 54.8%, respectively.
Sequential arrangement of adaptive feature refinement block
1
P2
27
55
80
86
91
93
2
P3
19
57
?
?
?
?
3
P4
10
40
56
?
?
?
4
P5
13
28
46
57
?
?
Parallel arrangement of adaptive feature refinement block
6
P2
22
59
75
85
88
95
7
P3
23.1
52
?
?
?
?
8
P4
18
43
64
?
?
?
9
P5
13
28
51
62
?
?
Tab.2
No#
Camera view
Division
Testing subjects
1
P1
Training
971
2
P2
Testing
306
3
P3
Testing
109
4
P4
Testing
193
5
P5
Testing
239
Tab.3
Fig.10
Sr. No.
Authors
Method
Rank 1 score
Datasets
1
Li et al. [16]
Deep learning
24.26
CUHK01 and CUHK03
2
Koestinger et al. [48]
Distance metric learning
20.8
VIPeR and ToyCars
3
Zheng et al. [49]
Bag of words model
24.33
CUHK03
4
Fan et al. [50]
Clustering and fine-tuning
24.8
CUHK03
5
Feng et al. [51]
Distance metric learning
21.96
CUHK03
6
Proposed method
Deep learning
24.6
CUHK01 and CUHK02
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