1. Marco Pedersoli, Jordi Gonzàlez, Xu Hu, and Xavier Roca
Toward Real-Time Pedestrian Detection Based on a Deformable Template Model
Marco Pedersoli, Jordi Gonzàlez, Xu Hu, and Xavier Roca
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 15, NO. 1, FEBRUARY

2. Outline Introduction Related work Methods Experiment Result Conclusion
System Overview
CtF Search(Course to Fine)
Ctf Search With Deformations
Small Pedestrians
Experiment Result
Conclusion

3. Introduction
In this paper, we deal with pedestrian detection using a single camera mounted on the vehicle.
Pedestrian is hard to detect because…
Pedestrians have a much broader appearance variability than other objects.
Pedestrian detection is much more error critical.

4. Introduction High accuracy techniques need plenty of time.
Some speedups can be achieved by restricting detection.

5. Introduction
We use a multiresolution representation and the coarse-to-fine (CtF) strategy to speed up the search.
Add a binary variable to detect small pedestrian.

6. Related work
In more recent years, object detection has shown great improvements.
It is possible to detect pedestrians using single images.
In the template matching(TM) model, an object is represented by a learned template.
Recent examples of detectors based on this technique can be
found in [5] and [20]–[22].

7. Related work
[5] P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part based models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 9, pp. 1627–1645, Sep [20] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. IEEE CVPR, 2005, pp. 886–893. [21] A. Vedaldi and A. Zisserman, “Structured output regression for detection with partial occlusion,” in Proc. NIPS, 2009, pp. 1–9. [22] L. Zhu, Y. Chen, A. Yuille, and W. Freeman, “Latent hierarchical structural learning for object detection,” in Proc. IEEE CVPR, 2010, pp. 1062–1069.

8. Related work
In [29], a cascade of parts to speed up deformable object detection is proposed.
[29] P. F. Felzenszwalb, R. Girshick, and D. McAllester, “Cascade object
detection with deformable part models,” in Proc. IEEE CVPR, 2010,
pp. 2241–2248.

9. Related work
In [30], the authors propose to use GPU computation and ground- plane constraints to also obtain a real-time system for pedestrian detection.
[30] P. Sudowe and B. Leibe, “Efficient use of geometric constraints for sliding-window object detection in video,” in Proc. ICVS, 2011, pp. 11– 20.

10. Proposed methods - System Overview
Given an image, we precompute the HOG features of the image at different resolutions, obtaining a pyramid of HOGs.
2. The pyramid is scanned at all resolutions in a CtF way, finding the locations that are the most similar to the template.
3. These locations are further processed by applying nonmaximum suppression (NMS) to the overlapping ones.

11. Proposed methods - System Overview

12. Course to Fine(CtF) Search
The standard procedure to find an object in the image consists of evaluating the similarity between the object model and the image features at every location and scale in the image.
M: Object Model , H: HOG feature
x = (x, y, s), where x and y are the coordinates of the window and s is its scale

13. Course to Fine(CtF) Search
The complete scan over positions and scales is very expensive.
CtF search can save computation but still obtain results very similar to the complete search.

14. Course to Fine(CtF) Search
The key idea is to decompose the search over multiple resolutions,
i.e., from coarse and then fine.

15. Course to Fine(CtF) Search
The score of the multiple-resolution detector is computed as sum over resolutions r of the object model Mr with the corresponding features H.

16. Course to Fine(CtF) Search

17. CtF Search With Deformations
Object deformations may be produced by viewpoint changes or articulated movements such as limb movements.
Adding moving parts allows the detector to better adapt to local object deformations.

18. CtF Search With Deformations
In the object model, each resolution level is further divided into parts.

19. CtF Search With Deformations
dx and dy that represent the displacement of a part p with respect to its father.

20. Small Pedestrians
Detecting far pedestrian gives enough time for a proper action to avoid collision.
Pedestrians far from the vehicle correspond to low-resolution pedestrians.
When the number of pixels representing an object is low, the ability to recognize the object is highly reduced.

21. Small Pedestrians
In CtF search a small instance object does not have fine-resolution features, but it still has the coarse representation.
As the resolution goes high , the HOG feature may disappear, and the missing high resolution features are set to zero.
In this way detections of small objects would have a score that is lower.

22. Small Pedestrians To overcome this, the score is computed as
hr is a binary variable.
When the corresponding HOG features H(xr,p) are missing and therefore set to 0.
That makes scores of detections generated without high-resolution features.

23. Experiment Result
We evaluate our method on the Computer Vision Center (CVC)-02 data set, which is a data set specific for pedestrian detection in the context of driving assistance.

24. Experiment Result
CtF configuration with deformations and “resolution” feature activated.
(False Positive Per Image)

25. Experiment Result- Rigid Versus Deformable Models
Average precision ↑ but needs more time

26. Experiment Result - CtF Versus Complete Search
Compared with complete search, the scan time ↓ and the average precision
is almost the same.

27. Experiment result - Detection of Small Pedestrians

28. Conclusion
This method is based on the combination of recent state-of-the art techniques for fast and accurate object detection.
Three useful techniques mentioned make this method perform well.