Human Detection in Video over Large Viewpoint Changes

Recall
Recall
Recall
Recall
Recall
Recall
1258 G. Duan, H. Ai, and S. Lao
1
Test Sequence 1 of ours
1
Test Sequence 2 of ours
0.75
S05 of PETS2007
0.95
0.95
0.7
0.9
0.9
0.65
0.85
0.85
0.6
0.8
0.75
0.7
0.65
0.05 0.1 0.15 0.2 0.25 0.3
FPPI
1
1/2
1/3
1/4
1/5
0.8
0.75
0.7
1
1/2
1/3
0.65
1/4
1/5
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
FPPI
0.55
0.5
0.45
0.4
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
FPPI
1
1/2
1/3
1/4
1/5
0.7
Seq.#1 of ETHZ
0.9
Seq.#2 of ETHZ
0.8
Seq.# 3 of ETHZ
0.65
0.8
0.75
0.6
0.7
0.55
0.7
0.65
0.5
0.6
0.6
0.55
0.45
0.5
0.5
0.4
0.35
0.3
0.25
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2
FPPI
1
1/2
1/3
1/4
1/5
0.4
0.3
0.2
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
FPPI
1
1/2
1/3
1/4
1/5
0.45
1
0.4
1/2
1/3
0.35
1/4
1/5
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
FPPI
Fig. 7: The robustness evaluation of our approach to video frame rate changes. 1 represents
the original frame rate, 1/2 represents that the testing dataset is downsampled
to 1/2 original frame rate and so as 1/3, 1/4 and 1/5.
Compared to [20] [22], our approach is better. Furthermore, we have not used
any additional cues like depth maps, ground-plane estimation, and occlusion reasoning,
which are used in [20]. “HOG, IMHwd and HIKSVM” proposed in [4],
combines HOG feature [18] and the Internal Motion Histogram wavelet difference
(IMHwd) descriptor [3] together using histogram intersection kernel SVM
(HIKSVM) and achieves the best results than other approaches used in [4]. Our
approach is not as good as but comparable to “ HOG, IMHwd and HIKSVM”and
we argue that our approach is much simpler and faster. Currently, our approach
takes about 0.29s on ETHZ, 0.61s on PETS and 0.55s on our dataset to process
one frame in average.
In order to evaluate the robustness of our approach to video frame rate
changes, we downsample the videos to their 1/2, 1/3, 1/4 and 1/5 original frame
rate to compare with the original frame rate. Our approach is evaluated on
the testing datasets with frame rate changes, and the ROC curves are shown in
Fig. 7. The results on the three sequences of ETHZ dataset and S05 of PETS2007
are similar, but the results on our collected two sequences differ a lot. The main
reason is that: 1) as frame rates get lower, human motion changes more abruptly;
2) note that human motion in near VV videos changes more fiercely than that
in near HV or SV ones. Our collected two sequences are near VV, so relatively
human motion changes even more drastically than the other testing datasets.
Generally speaking, our approach is robust to the video frame rate change to a
certain extent.
Discussions. We then discuss about the selection of EMC-Boost instead of
MC-Boost and a general Boost algorithm.
MC-Boost runs several classifiers together at any time, which may be good at
classification problems, but not at detection problems. Considering the sharing