Human detection based on Sliding Window Approach

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1 Human detection based on Sliding Window Approach Heidelberg University Institute of Computer Engeneering Seminar: Mobile Human Detection Systems Name: Njieutcheu Tassi Cedrique Rovile Matr.Nr: Automation Laboratory, Institute of Computer Engineering, Heidelberg University

2 Motivation [4] [6] [5] [7] Automation Laboratory, Institute of Computer Engineering, Heidelberg University 2

3 Outline I. problem formulation II. solution approach III. methods IV.experimental evaluation V. summary VI.conclusion & future works Automation Laboratory, Institute of Computer Engineering, Heidelberg University 3

4 Problem formulation given is a snapshot from a fixed camera on a mobile agent required is an algorithm to detect human in given image data [8] Automation Laboratory, Institute of Computer Engineering, Heidelberg University 4

5 Solution approach input image I downsampling S for each image segmentation based on sliding window W filtering M human non-human I: image data S : set of images W: set of detection windows Y for each detection windows classification HOG features with SVM V extraction M: subset of detection windows V: HOG features vector Y= {-1,1} -1 non-human 1 human Automation Laboratory, Institute of Computer Engineering, Heidelberg University 5

6 deal with different human heights reduce image resolution fixed scale factor k l(m,n) low pass filter f(m,n) k d(m,n) = f(km,kn) f(8,8) Methods: image downsampling k = 2 d(4,4) Automation Laboratory, Institute of Computer Engineering, Heidelberg University 6 [9]

7 Methods: image segmentation moving window with fixed size (64x128 pixels) slide with small strides in x & y directions each detection window is separately classified large number of windows to be classified high computational cost slide [9] Automation Laboratory, Institute of Computer Engineering, Heidelberg University 7

8 Methods: windows filtering [2] reduce number of detection windows computational cost reduction discard unlikely windows filtering techniques: (1) entropy filter (2) magnitude filter Automation Laboratory, Institute of Computer Engineering, Heidelberg University 8

9 Methods: windows filtering (1) entropy filter reject window no W for each window extract histogram of gradient orientation H compute entropy E E > T? yes W: set of detection windows H: bins vector (1x9) E: entropy value T: threshold value add current window to subset windows Automation Laboratory, Institute of Computer Engineering, Heidelberg University 9

10 Methods: windows filtering (2) magnitude filter reject window no W for each window compute gradient magnitude G compute mean of gradient magnitude M M > T? yes W: set of detection windows G: gradient magnitude image M: mean value T: threshold value add current window to subset windows Automation Laboratory, Institute of Computer Engineering, Heidelberg University 10

11 Methods: windows filtering normal image entropy filter magnitude filter [2] Automation Laboratory, Institute of Computer Engineering, Heidelberg University 11

12 Methods: features extraction [1] [3] histograms of oriented gradients (HOG) feature is used as human descriptor HOG features extraction steps: a) gradient computation b) cell orientation histograms c) block normalization Automation Laboratory, Institute of Computer Engineering, Heidelberg University 12

13 [9] Methods: features extraction a) gradient computation G x = I [-1,0,1] x G y = I [-1,0,1] T subimage of 4x4 pixels (I) y G x = -3 G y = 0 µ = 3 Θ = Automation Laboratory, Institute of Computer Engineering, Heidelberg University 13

Methods: features extraction b) cell orientation histograms gradient direction gradient magnitude 80 36 10 10 64 90 90 73 3 3 4 6 6 7 9 8 37 9 9 179 78 27 169 166 5 2 5 45 4 8 1 2 87 136 173 39 102

14 Methods: features extraction b) cell orientation histograms gradient direction gradient magnitude [9] histogram of gradient [10] vote to bin 8 bin_8 = 120x x Θ Automation Laboratory, Institute of Computer Engineering, Heidelberg University 14 bins

15 Methods: features extraction c) block normalization block 1 block 2 cells H(C11) H(C21) H(C12) H(C22) H(C12) H(C22) H(C13) H(C23) block 1 feature vector block 2 feature vector... block n [9] block 1 block 2... block n HOG feature vector Automation Laboratory, Institute of Computer Engineering, Heidelberg University 15

block overlaping 50 % bin size 9 descriptor size 34596 features 08.03.

16 Methods: features extraction [9] HOG feature visualization HOG parameter value window size 256x256 pixels cell size 8x8 pixels block size 2x2 cells block overlaping 50 % bin size 9 descriptor size features Automation Laboratory, Institute of Computer Engineering, Heidelberg University 16

17 Methods: image classification classification based on linear support vector machine(svm) 2-class classification: -1 non-human 1 human learn mapping: X Y x X is some HOG feature vector(1xn), x R N y Y is a class label decision rule 1 g(x), if x human g(x) -1, if x non human X 2 X Automation Laboratory, Institute of Computer Engineering, Heidelberg University 17

18 miss rate at 10 0 FPPI number of windows produced Experimental evaluation scaling factor [2] miss rate of detector at 10 0 false positive per image (FPPI) with different scale factor 0,35 0,34 0,33 0,34 0,33 tradeoff between scale factor and number of windows generated for a 640x480 image ,32 0,31 0,30 0,29 0,31 0, ,28 0, ,26 1,15 1,10 1,05 1,01 scale factor 0 1,15 1,10 1,05 1,01 scale factor Automation Laboratory, Institute of Computer Engineering, Heidelberg University 18

19 miss rate at 10 0 FPPI recall Experimental evaluation windows filtering [2] scale factor = 1.15 total number of detection windows = ,7 0,6 0,5 0,4 0,3 0,2 0,1 0,0 miss rate at 10 0 false positive per image (FPPI) applying a filter on the detector 0,39 0,45 0,58 0,34 0,35 0,38 31% 41% 53% 38% 44% 54% entropy filter magnitude filter percentage of discarded detection windows relationship between rejection percentage and recall achieved by filters (assuming an ideal detector) 1,2 1 0,8 0,6 0,4 0,2 0 8% 30% 50% 70% 90% percentage of rejected windows entropy filter magnitude filter Automation Laboratory, Institute of Computer Engineering, Heidelberg University 19

20 miss rate miss rate Experimental evaluation detectors performance performance obtained by detector using magniute or entropy filter [2] 1,0 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0,0 0,004 0,14 0,2 1,2 1,8 false positives per image (FPPI) performance obtained by detector without filter [1] 0,26 0,21 0,16 0,11 0,06 0,01 1,00E-6 1,40E-5 1,70E-4 1,00E-2 false positives per window (FPPW) Automation Laboratory, Institute of Computer Engineering, Heidelberg University 20

21 Summary image is scanned at all scale & location detection windows filtering with magnitude or entropy filter histograms of oriented gradients as human descriptor linear support vector machines as classifier Automation Laboratory, Institute of Computer Engineering, Heidelberg University 21

22 conclusion & future works conclusion small scaling factor improve detector performance, but increase number of detectors windows magnitude filter is better than entropy filter filtering discard a large number of detection windows with a slight reduction on recall future works: adjust detection windows to person location employ filter after classification to remove possible false positives filtering based on motion detection Automation Laboratory, Institute of Computer Engineering, Heidelberg University 22

23 Thank for your attention! any questions? Automation Laboratory, Institute of Computer Engineering, Heidelberg University 23

24 References [1] Navneet Dalal and Bill Triggs, histograms of oriented gradients for human detection, CVPR 2005 [2] Artur Jordão Lima Correia, Victor Hugo Cunha de Melo, and William Robson Schwartz, a study of filtering approaches for sliding window pedestrian detection [3] Carlo Tomasi, histograms of oriented gradients [4] S60-Pedestrian-Detection.jpg [5] [6] [7] [8] [9] [10] Automation Laboratory, Institute of Computer Engineering, Heidelberg University 24

Bonus Slide-Methods: classifier learning HOG parameter window size cell size block size value 64x128 pixels 8x8 pixels 16x16 cells block overlaping 50 % bin size 9 descriptor size training set test

25 Bonus Slide-Methods: classifier learning HOG parameter window size cell size block size value 64x128 pixels 8x8 pixels 16x16 cells block overlaping 50 % bin size 9 descriptor size training set test set 3780 features INRIA person dataset 614 positive images 1218 negative images 1208 positive windows 288 positive images 453 negative images 566 positive windows possitive window human class window data (w) extract HOG feature learn SVM negative window feaure vector (x) 1 g(x) g(x) -1 non-human class Automation Laboratory, Institute of Computer Engineering, Heidelberg University 25

26 Bonus Slide experimental evaluation [1] how does block overlapping affect detection performance? how to select the sliding windows size? Automation Laboratory, Institute of Computer Engineering, Heidelberg University 26

Mobile Human Detection Systems based on Sliding Windows Approach-A Review

Mobile Human Detection Systems based on Sliding Windows Approach-A Review Seminar: Mobile Human detection systems Njieutcheu Tassi cedrique Rovile Department of Computer Engineering University of Heidelberg