Signal Processing - September 2016 - 91
Compression
Factor
Compressive Recognition
Reconstruct-ThenRecognize (IDT)
Recognition
Rate
Run Time
Recognition
Rate
Run Time
1
81.11%
3.22 seconds
100%
3.1
100
81.11%
3.22 seconds
5.56%
1,520 seconds
200
81.11%
3.07 seconds
10%
1,700 seconds
300
76.66%
3.1 seconds
10%
1,800 seconds
500
78.99%
3.08 seconds
7.77%
2,000 seconds
Figure 7. (a) The spatial localization of actions without reconstruction at a compression ratio of 100 for different actions in the Weizmann data set. (b)
The table compares recognition rates and runtimes for direct recognition in the compressive domain versus reconstruct-then-recognize. Direct recognition
outperforms the reconstruct-then-recognize with IDT method and achieves a recognition rate that is comparable to the recognition rate of 81.11% in the
case of OM [1], [29] and is much faster. (Figure adapted from [22].)
set with scale and viewpoint variations. For testing, we use
leave-one-out cross validation. At the compression ratios of
100 and 300, the recognition rates are 70.67% and 68%,
respectively. The rates obtained are comparable to those
obtained in the OM setup [29] (i.e., 69.2%). Considering the
difficulty of the data set, these results are very encouraging.
Sample results of the spatial localization and confusion
matrix for recognition are shown in Figure 8.
Learning tuned measurement operators for task-specific inference
While the results presented previously indicate that effective recognition of actions is feasible from compressive
imagers without the need for reconstruction, the results
suggest that one can potentially achieve better results with
a task-specific design of measurement matrices. As already
indicated at the beginning of the experiments section, taskspecific design can be achieved in a number of ways using
Action
Golf
Swing
Kicking
Riding
Horse
Run
Side
Golf swing
77.78
16.67
0
0
0
Kicking
0
75
0
5
Riding
Horse
16.67
16.67
41.67
Run Side
0
0
Skateboarding
0
Swing
Skateboarding Swing
Walk
Driving
Lifting
0
5.56
0
0
5
10
5
0
0
8.33
8.33
0
8.33
0
0
0
61.54
7.69
15.38
7.69
7.69
0
8.33
8.33
25
50
0
5
0
0
0
3.03
12.12
0.08
3.03
78.79
3.03
0
0
Walk
0
9.09
4.55
4.55
9.09
9.09
63.63
0
0
Driving
0
0
0
0
7.14
0
0
92.86
0
Lifting
0
0
0
0
0
0
0
16.67
83.33
Figure 8. (a) The reconstruction-free spatial localization of the subject for OM (shown as a yellow box) and spatiotemporal smashed filter (STSF) (shown
as a green box) at a compression ratio of 100 for some correctly classified instances of various actions in the UCF sports data set. (b) The confusion
matrix for UCF sports database at a compression factor of 100. The recognition rate for this scenario is 70.67%, which is comparable to OM [29] (69.2%).
(Figure adapted from [22].)
IEEE SIgnal ProcESSIng MagazInE
|
September 2016
|
91
Table of Contents for the Digital Edition of Signal Processing - September 2016
Signal Processing - September 2016 - Cover1
Signal Processing - September 2016 - Cover2
Signal Processing - September 2016 - 1
Signal Processing - September 2016 - 2
Signal Processing - September 2016 - 3
Signal Processing - September 2016 - 4
Signal Processing - September 2016 - 5
Signal Processing - September 2016 - 6
Signal Processing - September 2016 - 7
Signal Processing - September 2016 - 8
Signal Processing - September 2016 - 9
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Signal Processing - September 2016 - 89
Signal Processing - September 2016 - 90
Signal Processing - September 2016 - 91
Signal Processing - September 2016 - 92
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Signal Processing - September 2016 - 94
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Signal Processing - September 2016 - 101
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Signal Processing - September 2016 - 129
Signal Processing - September 2016 - 130
Signal Processing - September 2016 - 131
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Signal Processing - September 2016 - 133
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Signal Processing - September 2016 - 135
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Signal Processing - September 2016 - 148
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Signal Processing - September 2016 - 150
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Signal Processing - September 2016 - Cover3
Signal Processing - September 2016 - Cover4
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