A Real-time Model for Multiple Human Face Tracking from Low-resolution Surveillance Videos

AbstractThis article discusses a novel approach of multiple-face tracking from low-resolution surveillance videos. There has been significant research in the field of face detection using neural-network based training. Neural network based face detection methods are highly accurate, albeit computationally intensive. Hence neural network based approaches are not suitable for real-time applications. The proposed approach approximately detects faces in an image solely using the color information. It detects skin region in an image and finds existence of eye and mouth region in the skin region. If it finds so, it marks the skin region as a face and fits an oriented rectangle to the face. The approach requires low computation and hence can be applied on subsequent frames from a video. The proposed approach is tested on FERET face database images, on different images containing multiple faces captured in unconstrained environments, and on frames extracted from IP surveillance camera

Low-Resolution Vision for Autonomous Mobile Robots

The goal of this research is to develop algorithms using low-resolution images to perceive and understand a typical indoor environment and thereby enable a mobile robot to autonomously navigate such an environment. We present techniques for three problems: autonomous exploration, corridor classification, and minimalistic geometric representation of an indoor environment for navigation. First, we present a technique for mobile robot exploration in unknown indoor environments using only a single forward-facing camera. Rather than processing all the data, the method intermittently examines only small 32X24 downsampled grayscale images. We show that for the task of indoor exploration the visual information is highly redundant, allowing successful navigation even using only a small fraction (0.02%) of the available data. The method keeps the robot centered in the corridor by estimating two state parameters: the orientation within the corridor and the distance to the end of the corridor. The orientation is determined by combining the results of five complementary measures, while the estimated distance to the end combines the results of three complementary measures. These measures, which are predominantly information-theoretic, are analyzed independently, and the combined system is tested in several unknown corridor buildings exhibiting a wide variety of appearances, showing the sufficiency of low-resolution visual information for mobile robot exploration. Because the algorithm discards such a large percentage (99.98%) of the information both spatially and temporally, processing occurs at an average of 1000 frames per second, or equivalently takes a small fraction of the CPU. Second, we present an algorithm using image entropy to detect and classify corridor junctions from low resolution images. Because entropy can be used to perceive depth, it can be used to detect an open corridor in a set of images recorded by turning a robot at a junction by 360 degrees. Our algorithm involves detecting peaks from continuously measured entropy values and determining the angular distance between the detected peaks to determine the type of junction that was recorded (either middle, L-junction, T-junction, dead-end, or cross junction). We show that the same algorithm can be used to detect open corridors from both monocular as well as omnidirectional images. Third, we propose a minimalistic corridor representation consisting of the orientation line (center) and the wall-floor boundaries (lateral limit). The representation is extracted from low-resolution images using a novel combination of information theoretic measures and gradient cues. Our study investigates the impact of image resolution upon the accuracy of extracting such a geometry, showing that centerline and wall-floor boundaries can be estimated with reasonable accuracy even in texture-poor environments with low-resolution images. In a database of 7 unique corridor sequences for orientation measurements, less than 2% additional error was observed as the resolution of the image decreased by 99.9%

A Robust Adaboost-based Algorithm For Low-resolution Face Detection

This work presents a face detection algorithm based on Multiscale Block Local Binary Patterns (MB-LBP) and an improved AdaBoost algorithm. The proposed boosting algorithm is capable of avoiding sample overfitting over its training process. This goal is achieved by making use of the information of sample misclassification frequency to update the weight distribution in the training process. Experimental results evidence some advantages of the proposed method over the classical AdaBoost algorithms, including the generalization capacity, overfitting avoidance and high precision rate on low-resolution images. © 2012 Springer-Verlag.7435 LNCS366373Viola, P., Jones, M.J., Robust real-time face detection (2001) Int. J. Comput. Vision, 57, pp. 137-154Li, G., Xu, Y., Wang, J., An improved adaboost face detection algorithm based on optimizing skin color model (2010) 2010 Sixth International Conference on Natural Computation (ICNC), 4, pp. 2013-2015. , AugustHayashi, S., Hasegawa, O., Detecting Faces from Low-Resolution Images (2006) LNCS, 3851, pp. 787-796. , Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006, Part I. Springer, HeidelbergOjala, T., Pietikainen, M., Maenpaa, T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns (2002) IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (7), pp. 971-987Liao, S., Zhu, X., Lei, Z., Zhang, L., Li, S.Z., Learning Multi-scale Block Local Binary Patterns for Face Recognition (2007) LNCS, 4642, pp. 828-837. , Lee, S.-W., Li, S.Z. (eds.) ICB 2007. Springer, HeidelbergDietterich, T.G., Ensemble Methods in Machine Learning (2000) LNCS, 1857, pp. 1-15. , Kittler, J., Roli, F. (eds.) MCS 2000. Springer, HeidelbergRätsch, G., Onoda, T., Müller, K.-R., Soft margins for adaboost (2001) Mach. Learn., 42, pp. 287-320Servedio, R.A., Smooth boosting and learning with malicious noise (2003) J. Mach. Learn. Res., 4, pp. 633-648(2012) PICS: Psychological Image Collection at Stirling, , http://pics.stir.ac.uk, January(2012) UMIST: Face Database, , http://www.sheffield.ac.uk/eee/research/iel/research/face, January(2012) BioID: Face Database, , https://www.bioid.com/download-center/software/bioid-face-database.html, January(2012) FEI: Face Database, , http://fei.edu.br/~cet/facedatabase.html, JanuarySamaria, F.S., Samaria, F.S., Harter, A., Site, O.A., (1994) Parameterisation of a Stochastic Model for Human Face IdentificationRowley, H., Baluja, S., Kanade, T., (1997) Rotation Invariant Neural Network-based Face Detection, , Technical Report CMU-CS-97-201, Computer Science Department, Pittsburgh, PA DecemberFrischholz, R., (2012) Bao Face Database at the Face Detection Homepage, , http://www.facedetection.com, Januar