Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages

1. Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages Yu-Ting Chen and Chu-Song Chen, Member, IEEE

2. INTRODUCTION • TECHNIQUES for detecting humans in images have a wide variety of applications, such as video surveillance， smart rooms， content-based image/video retrieval， and intelligent transportation systems (ITS)。

3. ABSTRACT • We propose a method that can detect humans in a single image based on a novel cascaded structure . • USE : intensity-based rectangle features， gradient-based 1-D features， real AdaBoost algorithm， a novel cascaded structure (standard boosted cascade)， meta-stages。

4. REAL ADABOOST AND FEATURE POOL • Intensity-Based Features: :denotes the rth type rectangle feature (r=1~10) and are the Illumination summations in the white and black regions, : is the feature value in block The integral-image method is used for fast evaluation of these features,but the results for human detection are not satisfactory.so add discrimination In each block,a featurevalue can be calculated

5. REAL ADABOOST AND FEATURE POOL • Gradient-Based Features: HOG: First, the representation is too complex to evaluate, resulting in a slow detection speed. Second,all the dimensions of a HOG feature vector are employed simultaneously, so it is not possible to just use some of them to achieve efficient detection. Third, its computation cost is high since it uses a Gaussian-kernel SVM instead of linear SVM. EOH: A EOH feature can only characterize one orientation at a time, and it is represented by a real value. Many EOH features (with respect to different orientations)can be extracted from an image region, but each feature is only 1-D.

6. REAL ADABOOST AND FEATURE POOL • EOF Features: First: The gradient image is calculated from the original image by convolving the edge operator.

7. REAL ADABOOST AND FEATURE POOL Second: To compute the EOH features, the pixel gradient magnitude m and gradient orientationθ of each pixel p at location (x,y) in block Bi. Gx: gradients in the horizontal directions Gy: gradients in the vertical directions The gradient orientation is evenly divided into K bins over 0 to 180 . The sign of the orientation is ignored; thus,the orientations between 180 to 360 are deemed the same as those between 0 and 180 .

8. REAL ADABOOST AND FEATURE POOL Third: The gradient orientation histograms Ｅi,k in each orientation bin K of block Bi are obtained by summing all the gradient magnitudes whose orientations belong to bin K in Bi. Fourth: is the feature value of the Kth ( K = 1~ K) EOH feature in block Bi. ε is a small positive value that avoids the denominator being zero.

9. REAL ADABOOST AND FEATURE POOL Gradient-Based ED (edge-density) feature : For a block , an ED (edge-density) feature is defined as the average gradient magnitude is the ED feature value in Bi and ai is the area of Bi Similar to the rectangle features, the integral-image method can be employed for fast evaluation of the ED features. Combined Feature Pool: r = 1~10, k = 1~K

10. REAL ADABOOST AND FEATURE POOL • real AdaBoost algorithm : Given input data z and its feature Value f(z), the weak learner output h(z) After selecting T weak classifiers,the strong classifier of Real AdaBoost can be expressed as α is a threshold A high confidence value implies that the input data is likely to be a positive sample.

11. CASCADING FEED-FORWARD CLASSIFIERS A.Contains S stages and Ai is referred to as an AdaBoost classifier in the ith stage. B.In this cascaded structure, detection windows that do not contain humans C.To find an object of unknown position and size in an image usually involves a brute-force search of all possible sites and scales in the image. Since there are usually far more negative windows than positive windows to detect in an image, saving on the detection time of the negative windows increases the overall efficiency of the object detector. DSince more difficult negative examples are used for training in later stages. In the current stage will not be used in later stages.

12. CASCADING FEED-FORWARD CLASSIFIERS To train a cascaded structure, the goals of the minimum detection rate of positive examples, di, and the maximum false-acceptance rate of negative examples,fi , are set for each stage Ai.

13. CASCADING FEED-FORWARD CLASSIFIERS • Adding Meta-Stages : “A ” and “M ” denote the AdaBoost stages and meta-stages Meta-stages: 2-D space Mi: 2-D vector

14. CASCADING FEED-FORWARD CLASSIFIERS • Meta-Stage Classifier: • we choose the linearSVM(LSVM) as the meta-stage classifier because of its high generalization ability and efficiency in evaluation. • ωis the 2-D normal vectorof the plane and • β is the offset from the origin. • The confidencevalue of the meta-stage for data is defined as

15. RESULTS • rectangle features = Rec-Cascade (625) • EOH features = EOH-Cascade (584) • ED features = ED-Cascade (2492) • a combination of rectangle and EOH features = RecEOH-Cascade(325) • a combination of them as feature = RecEOHED-Cascade (310)

16. RESULTS In our experiments, the maximum FPPW values are about 10^(-3) for most of the cascaded approaches compared. Since there are far more negative windows than the positive windows in an image, a detector shall have a very low false positive rate (e.g., under 10^(-3)), or it might not be practically useful. HOG add META than RecEOHED-Cascade MetaCascade-2D greatest miss rate versus false positives per window (FPPW)

17. RESULTS In our experiments, we consider thefollowing three forms: , 2-D meta classifiers ( n1 = 2, ni = 1 ) 3-D meta classifiers ( n1 = 3, ni = 2 ) 4-D meta classifiers ( n1 = 4, ni = 3 ) for a 320X240 testing image, the average processing speeds MetaCascade-2D = 8.61 fps (243) MetaCascade-3D = 8.52 fps (230) MetaCascade-4D = 8.44 fps (201) HOG-MetaCascade-2D = 6.12 fps (516)

18. RESULTS MetaCascade-2D = 8.61 fps MetaCascade-3D = 8.52 fps MetaCascade-4D = 8.44 fps HOG-MetaCascade-2D = 6.12 fps HOG-LSVM (0.91 fps)

19. RESULTS

20. Thank you for your listening ! 2008.10.21