Muhammad Hassan Khan

Multiple human detection in depth images

Most human detection algorithms in depth images perform well in detecting and tracking the movements of a single human object. However, their performance is rather poor when the person is occluded by other objects or when there are multiple humans present in the scene. In this paper, we propose a novel human detection technique which analyzes the edges in depth image to detect multiple people. The proposed technique detects a human head through a fast template matching algorithm and verifies it through a 3D model fitting technique. The entire human body is extracted from the image by using a simple segmentation scheme comprising a few morphological operators. Our experimental results on three large human detection datasets and the comparison with the state-of-the-art method showed an excellent performance achieving a detection rate of 94.53% with a small false alarm of 0.82%.

Automatic recognition of movement patterns in the vojta-therapy using RGB-D data

Vojta-therapy is a useful technique for the treatment of physical and mental impairments in humans, and is very effective for children of less than 6 months. During the therapy, a specific stimulation is given to the patients body to perform certain reflexive pattern movements. The repetition of this stimulation ultimately makes the previously blocked connections between the spinal cord and brain available, and after a few session, patients can perform these movements without any external stimulation. The treatment must be performed several times a day or week and can last for a few weeks or months. Therefore, the therapists may recommend an at-home continuation of the therapy. An automatic vision-based system is required which can analyze and verify the correct pattern of a patients body parts movement during the therapy process at home, ultimately revealing the accuracy of given treatment. We captured a dataset of more than 15,000 images from a Microsoft Kinect camera and a novel segmentation technique in RGB-D data is proposed to segment the patients body region from the scene using a k-means clustering algorithm. The movement patterns of a patients body parts are analyzed and a support vector machine (SVM) is trained to classify the correct movements. The classification results show that the proposed method is highly useful to recognize the correct movement patterns.

An automatic vision-based monitoring system for accurate Vojta-therapy

Vojta-therapy is a useful technique to treat the disorders in the central nervous and musculoskeletal system. During the therapy, a specific stimulation is given to the patients in order to cause the patients body to perform certain reflexive pattern movements. The repetition of this stimulation ultimately brings forth the previously blocked connections between the spinal cord and brain, and after a few sessions, patients can perform these movements without any external stimulation. In this paper we proposed an automatic vision-based monitoring system for accurate therapy. We proposed an infants (i.e., patient) detection and recognition of specific movements in his/her various body parts during the therapy process, using RGB-D data. First, A robust template matching based algorithm is exploited for infants detection using his/her head location. Second, various features are computed to capture the movements of different body parts during the therapy. In the classification stage, a multi-class support vector machine (mSVM) is used to classify the accurate movements of infant during the therapy process, which ultimately reveals the correctness of the given treatment. The proposed algorithm is evaluated on our challenging dataset, which was collected in a children hospital. The detection and classification results show that the proposed method is highly useful to recognize the correct movement pattern either in hospital or in-home therapy systems.

Semi-automatic Segmentation of Scattered and Distributed Objects

This paper presents a novel object segmentation technique to extract objects that are potentially scattered or distributed over the whole image. The goal of the proposed approach is to achieve accurate segmentation with minimum and easy user assistance. The user provides input in the form of few mouse clicks on the target object which are used to characterize its statistical properties using Gaussian mixture model. This model determines the primary segmentation of the object which is refined by performing morphological operations to reduce the false positives. We observe that the boundary pixels of the target object are potentially misclassified. To obtain an accurate segmentation, we recast our objective as a graph partitioning problem which is solved using the graph cut technique. The proposed technique is tested on several images to segment various types of distributed objects e.g. fences, railings, flowers. We also show some remote sensing application examples, i.e. segmentation of roads, rivers, etc. from aerial images. The obtained results show the effectiveness of the proposed technique.

Gait Recognition Using Motion Trajectory Analysis

Gait recognition has received significant attention in the recent years due to its applications in numerous fields of computer vision, particularly in automated person identification in visual surveillance and monitoring systems. In this paper, we propose a novel algorithm for gait recognition using spatio-temporal motion characteristics of a person. The proposed algorithm consists of four steps. First, motion features are extracted from video sequence which are used to generate a codebook in the second step. In a third step, the local descriptors are encoded using Fisher vector encoding. Finally, the encoded features are classified using linear Support Vector Machine (SVM). The performance of the proposed algorithm is evaluated and compared with state-of-the-art on two widely used gait databases TUM GAID and CASIA-A. The recognition results demonstrate the effectiveness of the proposed algorithm.

Stripes-Based Object Matching

We propose a novel and fast 3D object matching framework that is able to fully utilise the geometry of objects without any object reconstruction process. Traditionally, 3D object matching methods are mostly applied based on 3D models. In order to generate accurate and proper 3D models, object reconstruction methods are used for the collected data from laser or time-of-flight sensors. Although those methods are naturally appealing, heavy computations are required for segmentation as well as transformation estimation. Moreover, some useful features could be filtered out during the reconstruction process. On the contrary, the proposed method is applied without any reconstruction process. Building on stripes generated from laser scanning lines, we represent an object by a set of stripes. To capture the full geometry, we describe each stripe by the proposed robust point context descriptor. After representing all stripes, we perform a flexible and fast matching over all collected stripes. We show that the proposed method achieves promising results on some challenging real-life objects.

Spatiotemporal features of human motion for gait recognition

Gait is a novel biometric feature that offers human identification at a distance and without physical interaction with the imaging device. Moreover, it performs well even in low resolution which makes it ideal for use in numerous human identification applications, e.g.,visual surveillance, monitoring and access control systems. Most existing gait-based human identification solutions extract human body silhouettes, contours or shapes from the images and construct gait features. Therefore, the performance of such algorithms highly depends upon the accuracy of human body segmentation, which is still a challenging problem in the literature. In this paper, we propose a new gait recognition algorithm which uses the spatial and temporal motion characteristics of human gait for individual identification without needing the silhouette extraction. The proposed algorithm extracts a set of spatiotemporal local descriptors from the gait video sequences. The extracted descriptors are encoded using the Fisher vector encoding and Gaussian mixture model-based codebook. The encoded features are classified using a simple linear support vector machine to recognize the individuals. The proposed gait recognition method is evaluated on five widely used gait databases, including indoor (CMU MoBo, CASIA-B) and outdoor (NLPR, CASIA-C, TUM GAID) gait databases. The results reveal that our method showed excellent performance on all five databases and outperformed the state-of-the-art gait recognition approaches.

Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model

Movement analysis of infants’ body parts is momentous for the early detection of various movement disorders such as cerebral palsy. Most existing techniques are either marker-based or use wearable sensors to analyze the movement disorders. Such techniques work well for adults, however they are not effective for infants as wearing such sensors or markers may cause discomfort to them, affecting their natural movements. This paper presents a method to help the clinicians for the early detection of movement disorders in infants. The proposed method is marker-less and does not use any wearable sensors which makes it ideal for the analysis of body parts movement in infants. The algorithm is based on the deformable part-based model to detect the body parts and track them in the subsequent frames of the video to encode the motion information. The proposed algorithm learns a model using a set of part filters and spatial relations between the body parts. In particular, it forms a mixture of part-filters for each body part to determine its orientation which is used to detect the parts and analyze their movements by tracking them in the temporal direction. The model is represented using a tree-structured graph and the learning process is carried out using the structured support vector machine. The proposed framework will assist the clinicians and the general practitioners in the early detection of infantile movement disorders. The performance evaluation of the proposed method is carried out on a large dataset and the results compared with the existing techniques demonstrate its effectiveness.

A computer vision-based system for monitoring Vojta therapy

A neurological illness is t he disorder in human nervous system that can result in various diseases including the motor disabilities. Neurological disorders may affect the motor neurons, which are associated with skeletal muscles and control the body movement. Consequently, they introduce some diseases in the human e.g. cerebral palsy, spinal scoliosis, peripheral paralysis of arms/legs, hip joint dysplasia and various myopathies. Vojta therapy is considered a useful technique to treat the motor disabilities. In Vojta therapy, a specific stimulation is given to the patients body to perform certain reflexive pattern movements which the patient is unable to perform in a normal manner. The repetition of stimulation ultimately brings forth the previously blocked connections between the spinal cord and the brain. After few therapy sessions, the patient can perform these movements without external stimulation. In this paper, we propose a computer vision-based system to monitor the correct movements of the patient during the therapy treatment using the RGBD data. The proposed framework works in three steps. In the first step, patients body is automatically detected and segmented and two novel techniques are proposed for this purpose. In the second step, a multi-dimensional feature vector is computed to define various movements of patients body during the therapy. In the final step, a multi-class support vector machine is used to classify these movements. The experimental evaluation carried out on the large captured dataset shows that the proposed system is highly useful in monitoring the patients body movements during Vojta therapy.

A Vision-Based Method for Automatic Crack Detection in Railway Sleepers

In this paper, a method for automatic selection and classification of the sleeper cracks is presented. This method includes three main sequential steps of image pre-processing, sleeper detection and crack detection. Two approaches including rule-based method and template matching method in the frequency domain are proposed for the sleeper detection step. We utilize adaptive threshold binarization to handle challenging crack detection under non-uniform lightening condition and hierarchical structure for the decision making step. Two unsupervised classifiers are exploited to detect the cracks. The results show that the presented method has the overall detection rate with accuracy of at least 87 percent.