Float Cascade Method for Pedestrian Detection

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1 Float Cascade Method for Pedestrian Detection Yanwen Chong 1,2, Qingquan Li 1,2, Hulin Kuang 1,2,3, and Chun-Hou Zheng 4 1 State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan , China {ywchong,qqli,hlkuang}@whu.edu.cn 2 Engineering Research Center for Spatio-Temporal Data Smart Acquisition and Application, Ministry of Education of China, 129 Luoyu Road, Wuhan , China 3 School of Electronic Information, WuHan University, Bayi Road, Wuhan ,China 4 College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui , China ywchong@whu.edu.cn Abstract. A novel pedestrian detection method based on the Four Direction Features (FDF), called FloatCascade pedestrian detection, is proposed for the pedestrian detection problem, which can be applied to the pedestrian detection problem in a single image. The FDF can represent pedestrian well, and the computation cost is lower than the HOG s. FloatCascade applies the plus-l-minus-r method to select the effective cascade features to improve the detection performance, where l is fixed by experience, r is a float value which must be lower than l or quite to l. whether add features or not is decided by the detection rate, while whether subtract features or not is decided by error rate. Experimental results show that the MutualCascade method is more effective than Voila and Jones cascade and some other Adaboost-based method, and is comparable with HOG-based methods. It also demonstrates a higher performance compared with the state-of-the-art methods. Keywords: FDF, GAB, CAdaboost, FloatCascade, pedestrian detection. 1 Introduction Recently, pedestrian detection becomes one of the most interesting and potentially useful challenges for modern engineering [1-2]. Extracting more effective features and developing more powerful learning algorithms have always been the research focus for pedestrian detection problem. The FDF is a relatively simple feature [3], of which the computational complexity is between the Haar feature and HOG feature. The FDF can extract gradient feature of the pedestrian which is suitable for pedestrian detection. The cascaded Adaboost (CAdaboost) has been treated as an example of the successful combination of feature selection and learning for rapid pedestrian detection. There is selection redundancy in both the Adaboost stage and the cascade stage [4, 5]. D.-S. Huang et al. (Eds.): ICIC 2012, CCIS 304, pp , Springer-Verlag Berlin Heidelberg 2012

2 314 Y. Chong et al. Therefore developing an algorithm that can select the less redundant features in the cascade stage is very necessary. A novel wrapper method for FDF feature selection in CAdaboost is proposed in this paper, namely FloatCascade. It uses a kind of plus-l-minus-r [6] method in essence. Compared to the VJ cascade [4], the feature selected in the Floatcascade method is much more effective. The Floatcascade method ensure that the performance of the combination of the feature selected increases monotonically, by the plus-l-minus-r method, i.e. a backtrack mechanism, and can eliminate the redundancy of features to some extent. The thought of Floatcascade and FloatBoost are similar, but the feature selection does not happen in the same stage: in FloatBoost [5] the feature selection happens during the Adaboost stage, while Floatcascade selects features during cascade stage. 2 The FloatCascade Algorithm Given n training samples, {(x 1,y 1),(x 2,y 2),...,(x n,y n)}, where x i is one training ample, y i =±1 is the sample label. The Adaboost algorithm adopted in this paper is Gentle Adaboost (GAB). The classification function of the strong classifier H(x) n in the cascade is denoted by formula (1). b(x) R H n(x)=c(b 1(x),b 2(x),...b n(x)) (1) Where n is the base classifier corresponding to a selected feature in cascade stage, n is the number of the selected FDF features and C( ) is the classification function which expresses the relationship between the strong classifier and the features selected. The different expression form of the same strong classifier can give different guidance for feature selection in Adaboost stage or cascade stage. Formula (1) is used in this paper. The target of our algorithm for feature selection is that the classification performance should increase monotonously with the number of features selected increases. Inspired by plus-l-minus-r [6], SFFS [7] FloatBoost [5], a new feature selection method, called FloatCascade in CAdaBoost is proposed to select the more effective feature combination. FloatCascade uses a kind of plus-l-minus-r method in essence, here l not only can be changed dynamically, but also can be set to a constant value, r is changed dynamically in accordance with detection performance constraints. The feature selection of FloatCascade is divided into two steps: feature addition and feature subtraction. The judgment standard for adding features is whether the detection rate increases, and the judgment standard for subtracting features is whether the error rate decreases. Every cascade stage actually aims at getting a strong classifier whose performance meets the requirement through proper features selected. Suppose that H(x)stands n for the strong classifier which has selected n features, the new classifier H n+l(x)after adding l features is shown as (3):

3 Float Cascade Method for Pedestrian Detection 315 H n+l(x)=h n(x) H l(x)=c(b 1(x),b 2(x),...,b n+l(x)) (2) Where H(x)is l composed of l base classifiers which increases the detection rate DP progressively during the process of feature addition one by one. Only when the l feature addition step has been completed, the r feature subtraction step begins. The strong classifier n+l-r H (x) after removing r features is denoted as formula (4). H n+l-r(x)=h n+l(x)-h r(x)=c(b 1(x),b 2(x),...,b n+l-r(x)) (3) Where H(x)is r composed of the base classifiers which meets the demand that the error rate EP can be decreased progressively during the feature subtraction one by one. The details of the FloatCascade algorithm based on the FDF are shown as Algorithm 1. At the beginning of the GAB training process all features are sorted from low to high according to the error rate of each feature, which makes the following feature selection sequential and more convenient. What s more, during feature selection in FloatCascade, a kind of plus-l-minus-r method is adopted in essence. In this way, the more effective features can be selected efficiently. If this one cannot meet the condition, try the next one in the rest feature sequence until the performance meets requirement. In our proposed algorithm, a new updating mechanism for negative samples is utilized to ensure that the number of the negative samples in any level is the same as the original one during training. In order to provide more discriminative features, we extract the FDF in all kinds of scales and at different locations in training samples, and build a feature pool in advance to make the cascade feature selection to be more convenient. The updating mechanism is as follows: empty the negative samples, we add not only the negative samples which are false positives in the stage, but also negative samples from the optional negative samples library that are false positives after being detected through all former stages. Algorithm 1. The FloatCascade algorithm Input: minimum acceptable detection rate, maximum acceptable false positive rate in ith level of the cascade POS: set of positives NEG: set of negatives Ftarget: target overall false positive rate f : maximum acceptable false positive rate for each level of cascade d : minimum acceptable detection rate for each level of cascade F(i): false positive rate in ith level of cascade D(i): detection rate in the ith level of cascade Ftotal: total false positive rate up to now n i : the number of features had been selected, also the number of base classifiers DP : detection rate DP(ni): The detection rate of the collection of n i features FP :the false positive rate

4 316 Y. Chong et al. EP: the error rate EP(ni): The error rate of the collection of n i features EP(ni-1) : The error rate of any n-1 i feature collection among the ni features selected. Initialize: i=0, D(0)=1.0,F(0)=1.0, F total =1.0 While F total> F target i=i+1, F(i)=F(i-1), n=0. i While F(i)>f (1) Calculating classification performance (a) n=n+1, i i extract ni features to train a GAB strong classifiers, and test on the validation set to get detection rate DP, the false positive rate FP and the error rate EP. (b)decrease the threshold for the classifier until DP>d, record the DP, FP, EP, threshold and the corresponding parameters of features, and F(i)=FP, D(i)=DP. (c)sort all features. Choose the feature with the minimum error rate as the first selected feature. If F(i) f Complete this level. (2) Adding l features (a) n=n+1, i i add a feature in turn according to the error rate order from low to high every time. Training GAB with the ni features. Record DP(ni), DP (ni-1), EP(ni) and EP (ni-1). (b) if DP(ni) DP (ni-1) Add the feature, record related parameters. else Reject the feature, ni=ni-1, continue to select new features. Goto(2)(a) (c) until the number of features added has been up to l. Goto3(3)(a) (3) Subtracting r features (a) Subtract a feature from the ni features to get the combination of n- i 1 features every time, train and record related parameters (b) if EP(n i) EP(ni-1) Do not subtract the nthfeature, i update the F(i). else Choose the combination of n- i 1 features which owns the lowest error rate, remove the corresponding feature. Update F(i). (c) if F(i) f. Completed feature selection of this level. else Continue to add and delete features. Go to (3)(a) End Save the classifiers, and all related parameters of features. Update the negative samples. F total=ftotal F(i) End Output: i-level cascade classifiers.

5 Float Cascade Method for Pedestrian Detection Experiments and Analyses 3.1 Distribution of the Selected Features In Fig.1, we present the number of the number of FDF at each level. From the Fig.1 we can see the number of FDF at each level is few. By conducting some experiments, we know that the computational cost of one FDF (32*32) is 0.38 seconds, but the computational cost of one HOG (32*32) is seconds, the FDF is faster than HOG The number of FDF The level of the classifiers Fig. 1. The number of selected features at each level From Fig.2, we can find that features selected by our method are multi-scale and sparse; meanwhile, each feature can represent some part of person, such as the head, the legs, the arms and shoulders. (a) (b) (c) Fig. 2. (a)(b)(c) respectively show features selected in the second,4th and 10level 3.2 Comparison with the State of Arts We compare the classification results of our method with the state of the art on the INRIA human test set, including HOG+SVM method [8], v-hog+cadaboost [9], the COV+Logitboost method [10] and the v-hog+clml [2], using miss rate tradeoff False Positives Per Window (FPPW) on a log scale in Fig.3. Our method s points on curves in Fig. 3 are obtained from different cascade levels of the training processes. The curves of other methods are obtained from their reported results. As shown in Fig.3, our method reaches a much better performance than the HOG-based results on the INRIA dataset. Comparing with others at the FPPW rate of 10-5, our method achieves 2.7% miss rate, which is about 6.3% lower than the

6 318 Y. Chong et al HOG+SVM v-hog+cadaboost COV+Logiboost v-hog+clml Our 0.12 Miss rate False positives per window Fig. 3. The result comparison of our method with the state of arts on INRIA test set v-hog+clml method, about 13.3% lower than the HOG+SVM method, about 9.3% lower than Zhu s and about 7.3% lower than Tuzel s. In Fig. 4 we compare the detector performance of our method with those of the state-of-the-art methods on the INRIA pedestrian test set which are all mentioned in [1]. Our method s points on curves are obtained from detection results on INRIA test set. The curves of other methods are obtained from [1] miss rate shapelet PoseInv.30 VJ FirMine.20 HOG our.10 HOGLbp false positives per image Fig. 4. The comparison of our method with the state of arts on INRIA test set As shown in Fig. 4, our method reaches a much better performance than the other Adaboost-based method on the INRIA dataset, besides our method is comparable with the HOG method, and a slightly inferior to the HOGLBP method. From some experiments, we can know that the detection time of our method is 5.8 seconds (tested on a 480*640 image). All of our experiments are conducted on Matlab 2009a without optimization, if we use C, the time can be decreased. 3.3 Detection Performance on More Datasets To demonstrate the detection performance of our method on more test datasets, we choose SDL-A test set of 140 images [2], USC-C test set of 100 images and INRIA test set of 288 images. What shown in Fig.5 are the detection results on three datasets: the performance on USC-C is best, and that on SDL is worst with miss rate highest under the same FPPI.

Float Cascade Method for Pedestrian Detection 319 1.80 SDL INRIA USC-C.64 miss rate.50.40.30.20.10.05 10-4 10-3 10-2 10-1 10 0 10 1 false positives per image Fig. 5.

From the figure we can find that the FloatCascade classifiers can accurately detect upright pedestrians with different postures, under the complex background. Fig. 6.

7 Float Cascade Method for Pedestrian Detection SDL INRIA USC-C.64 miss rate false positives per image Fig. 5. Detection results on three datasets: Sdl, USC-C and INRIA 3.4 Detection Results Presentation In Fig.6, some detection results of the FloatCascade classifiers on INRIA test data are presented. From the figure we can find that the FloatCascade classifiers can accurately detect upright pedestrians with different postures, under the complex background. Fig. 6. Example of detection results of the FloatCascade classifier 4 Conclusion A novel pedestrian detection method, called FloatCascade pedestrian detection, based on the Four Direction Features is proposed in this paper. FloatCascade, a new cascade method, applies the plus-l-minus-r method to select cascade features, chooses the more effective combination of features which can increase the detection rate and decrease the false positive rate effectively. Experimental results show that the Float Cascade method can reach much better detection performance using less features than the state-of-the-art CAdaboost methods presented in this paper. The proposed pedestrian detection method in the paper can accurately detect upright pedestrians with different postures, under the complex background. With the selected proper features and with application of the integral figure theory, the pedestrian detection time is shortened. There still exists some deficiencies in the method of this paper: Firstly, though the selected features using the FloatCascade method is more effective than the VJ cascade, it is not the optimal combination of features because it does not consider the correlation among the features; Secondly, the method of this paper can detect the upright pedestrians without shading effectively, but is poor in detecting the non-upright pedestrians with shading; Finally, the method of this paper has poor performance for large crowd, which is need to be improved as well.

8 320 Y. Chong et al. Acknowledgement. This paper was supported by China Postdoctoral Science Foundation, LIESMARS Special Research Funding and the National Natural Science Foundation of China ( , ). References 1. Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian Detection: Evaluation of the State of the Art. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(2), (2011) 2. Xu, R., Jiao, J., Zhang, B., Ye, Q.: Pedestrian Detection in Images via Cascaded L1 Norm Minimization Learning Method. Pattern Recognition 45(3), (2012) 3. Soga, M., Hiratsuka, S.: Pedestrian Detection for a Near Infrared Imaging System. In: Proc. the 11th International IEEE Conference on Intelligent Transportation Systems, pp (2008) 4. Viola, P.A., Jones, M.J.: Robust Real-Time Face Detection. Intl. Journal of Computer Vision 57(2), (2004) 5. Li, S., Zhang, Z.: FloatBoost Learning and Statistical Face Detection. IEEE Trans. Pattern Analysis and Machine Intelligence 26(9), (2004) 6. Kittler, J.: Feature Set Search Algorithm. Pattern Recognition in Practice, (1980) 7. Pudil, P., Novovicova, J., Choakjarernwanit, N., Kittler, J.: A Comparative Evaluation of Floating Search Methods for Feature Selection. Technical Report VSSP-TR-5/92, University of Surrey, UK (1992) 8. Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1(3), pp (2005) 9. Zhu, Q., Avidan, S., Yeh, M., Cheng, K.T.: Fast Human Detection Using a Cascade of Histograms of Oriented Gradients. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2(3), pp (2006) 10. Tuzel, Q., Porikli, F., Meer, P.: Human Detection via Classification on Riemannian Manifolds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1 8 (2007)

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