Robust human detection with occlusion handling by fusion of thermal and depth images from mobile robot

In this paper, a robust surveillance system to enable robots to detect humans in indoor environments is proposed. The proposed method is based on fusing information from thermal and depth images which allows the detection of human even under occlusion. The proposed method consists of three stages, pre-processing, ROI generation and object classification. A new dataset was developed to evaluate the performance of the proposed method. The experimental results show that the proposed method is able to detect multiple humans under occlusions and illumination variations

Bayesian 3D model based human detection in crowded scenes using efficient optimization

In this paper, we solve the problem of human detection in crowded scenes using a Bayesian 3D model based method. Human candidates are first nominated by a head detector and a foot detector, then optimization is performed to find the best configuration of the candidates and their corresponding shape models. The solution is obtained by decomposing the mutually related candidates into un-occluded ones and occluded ones in each iteration, and then performing model matching for the un-occluded candidates. To this end, in addition to some obvious clues, we also derive a graph that depicts the inter-object relation so that unreasonable decomposition is avoided. The merit of the proposed optimization procedure is that its computational cost is similar to the greedy optimization methods while its performance is comparable to the global optimization approaches. For model matching, it is performed by employing both prior knowledge and image likelihood, where the priors include the distribution of individual shape models and the restriction on the inter-object distance in real world, and image likelihood is provided by foreground extraction and the edge information. After the model matching, a validation and rejection strategy based on minimum description length is applied to confirm the candidates that have reliable matching results. The proposed method is tested on both the publicly available Caviar dataset and a challenging dataset constructed by ourselves. The experimental results demonstrate the effectiveness of our approach. © 2010 IEEE.published_or_final_versionThe 2011 IEEE Workshop on Applications of Computer Vision (WACV 2011), Kona, HI., 5-7 January 2011. In Proceedings of WACV2011, 2011, p. 557-56

Crowd counting and segmentation in visual surveillance

Reference no. MP-PD.8In this paper, the crowd counting and segmentation problem is formulated as a maximum a posterior problem, in which 3D human shape models are designed and matched with image evidence provided by foreground/background separation and probability of boundary. The solution is obtained by considering only the human candidates that are possible to be un-occluded in each iteration, and then applying on them a validation and rejection strategy based on minimum description length. The merit of the proposed optimization procedure is that its computational cost is much smaller than that of the global optimization methods while its performance is comparable to them. The approach is shown to be robust with respect to severe partial occlusions. ©2009 IEEE.published_or_final_versionThe 16th IEEE International Conference on Image Processing (ICIP 2009), Cairo, Egypt, 7-10 November 2009. In International Conference on Image Processing Proceedings, 2009, p. 2573-257

Three-dimensional model-based human detection in crowded scenes

In this paper, the problem of human detection in crowded scenes is formulated as a maximum a posteriori problem, in which, given a set of candidates, predefined 3-D human shape models are matched with image evidence, provided by foreground extraction and probability of boundary, to estimate the human configuration. The optimal solution is obtained by decomposing the mutually related candidates into unoccluded and occluded ones in each iteration according to a graph description of the candidate relations and then only matching models for the unoccluded candidates. A candidate validation and rejection process based on minimum description length and local occlusion reasoning is carried out after each iteration of model matching. The advantage of the proposed optimization procedure is that its computational cost is much smaller than that of global optimization methods, while its performance is comparable to them. The proposed method achieves a detection rate of about 2% higher on a subset of images of the Caviar data set than the best result reported by previous works. We also demonstrate the performance of the proposed method using another challenging data set. © 2011 IEEE.published_or_final_versio

ACM Based ROI Extraction for Pedestrian Detection with Partial Occlusion Handling

AbstractPedestrian detection in video surveillance systems is an integral part of Advanced Driver Assistance Systems (ADAS). In this paper, a new method for efficient pedestrian detection is proposed. The proposed method uses ACM (Active Contour Model) for efficiently locating pedestrian position in each video frame and thereby speeding up the detection time. This method uses a combination of HOG (Histogram of Oriented Gradients) and LBP (Local Binary Patterns) as features for training a two level linear SVM (Support Vector Machine). The proposed method handles partial occlusion using a two-level SVM classifier and eliminates multiple detection using Non Maximum Suppression (NMS) algorithm. The performance analysis is done using INRIA Person dataset and CVC Partial Occlusion dataset; and it is found that the proposed method gives promising results in terms of detection accuracy and detection speed

Three hypothesis algorithm with occlusion reasoning for multiple people tracking

This work proposes a detection-based tracking algorithm able to locate and keep the identity of multiple people, who may be occluded, in uncontrolled stationary environments. Our algorithm builds a tracking graph that models spatio-temporal relationships among attributes of interacting people to predict and resolve partial and total occlusions. When a total occlusion occurs, the algorithm generates various hypotheses about the location of the occluded person considering three cases: (a) the person keeps the same direction and speed, (b) the person follows the direction and speed of the occluder, and (c) the person remains motionless during occlusion. By analyzing the graph, our algorithm can detect trajectories produced by false alarms and estimate the location of missing or occluded people. Our algorithm performs acceptably under complex conditions, such as partial visibility of individuals getting inside or outside the scene, continuous interactions and occlusions among people, wrong or missing information on the detection of persons, as well as variation of the person’s appearance due to illumination changes and background-clutter distracters. Our algorithm was evaluated on test sequences in the field of intelligent surveillance achieving an overall precision of 93%. Results show that our tracking algorithm outperforms even trajectory-based state-of-the-art algorithms

People detection in surveillance: Classification and evaluation

This paper is a postprint of a paper submitted to and accepted for publication in IET Computer Vision and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library and at IEEE Xplore.Nowadays, people detection in video surveillance environments is a task that has been generating great interest. There are many approaches trying to solve the problem either in controlled scenarios or in very specific surveillance applications. The main objective of this study is to give a comprehensive and extensive evaluation of the state of the art of people detection regardless of the final surveillance application. For this reason, first, the different processing tasks involved in the automatic people detection in video sequences have been defined, then a proper classification of the state of the art of people detection has been made according to the two most critical tasks, object detection and person model, that are needed in every detection approach. Finally, experiments have been performed on an extensive dataset with different approaches that completely cover the proposed classification and support the conclusions drawn from the state of the art.This work has been partially supported by the Spanish Government (TEC2011-25995 EventVideo)

Human detection in surveillance videos and its applications - a review

Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using background subtraction, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed

Real-time human detection in urban scenes: Local descriptors and classifiers selection with adaboost-like algorithms

This paper deals with the study of various implementations of the AdaBoost algorithm in order to address the issue of real-time pedestrian detection in images. We use gradient-based local descriptors and we combine them to form strong classifiers organized in a cascaded detector. We compare the original AdaBoost algorithm with two other boosting algorithms we developed. One optimizes the use of each selected descriptor to minimize the operations done in the image (method 1), leading to an acceleration of the detection process without any loss in detection performances. The second algorithm (method 2) improves the selection of the descriptors by associating to each of them a more powerful weak-learner – a decision tree built from the components of the whole descriptor – and by evaluating them locally. We compare the results of these three learning algorithms on a reference database of color images and we then introduce our preliminary results on the adaptation of this detector on infrared vision. Our methods give better detection rates and faster processing than the original boosting algorithm and also provide interesting results for further studies. 1

Human Detection using Feature Fusion Set of LBP and HOG

Human detection has become one of the major aspect in the real time modern systems whether it is driver-less vehicles or in disaster management or surveillance. Multiple approaches of machine learning are used to find an efficient and effective way of human detection. The proposed method is mainly applied to address the pose-variant problem of human detection. It reduces the redundancy problem which leads to a slow system. To solve the pose variant and redundancy problem, mutation and crossover concept has been applied over Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) feature set to generate final set . Then combination of feature fusion set of LBP and HOG are fed into Support Vector Machine (SVM) for classification purpose. To improve the performance of detector an unsupervised framework has been used for learning. For post-processing to suppress overlapping and redundant windows - Non-maximal suppression is used . For training and testing purpose, INRIA dataset has been used. The proposed method is compared with HOG, LBP, and HOG-LBP techniques, the result shows that our method outperforms these techniques