A vision-based method for detecting the cracks in the concrete sleepers of the railway tracks will be introduced in this paper. The method is able to detect and partially classify the cracks of the concrete sleepers in two successive steps based on the image processing and pattern recognition techniques. The method has been implemented on the acquired image data frames followed by the analysis, experimental, comparison results and evaluation. The presented results are reasonable which indicates the goodness of the introduced method. The preliminary results of this work have been presented in [A. Delforouzi, A. H. Tabatabaei, M. H. Khan and M. Grzegorzek, A vision-based method for automatic crack detection in railway sleepers, in Kurzynski, M., Wozniak, M., Burduk, R. (eds.), Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017, Polanica Zdroj, Poland. CORES 2017. Advances in Intelligent Systems and Computing, Vol. 578 (Springer, Cham, 2018), pp. 130–139, doi: 10.1007/978-3-319-59162-9_14].

This paper presents a novel cross-view gait recognition technique based on the spatiotemporal characteristics of human motion. We propose a deep fully-connected neural network with unsupervised learning which transfers the gait descriptors from multiple views to the single canonical view. The proposed non-linear network learns a single model for all videos captured from different viewpoints and finds a shared high-level virtual path to map them on a single canonical view. Therefore, the model does not require any labels or viewpoint information in the learning phase. The network is learned only once using the spatiotemporal motion features of the gait sequences from several viewpoints, later it is used to construct the cross-view gait descriptors for the gallery and the probe sets. The descriptors are classified using simple linear support vector machine. Experiments carried out on the benchmark cross-view gait dataset, CASIA-B, and comparisons with the state-of-the-art demonstrate that the proposed method outperforms the existing cross-view gait recognition algorithms.

Biometric gait recognition has received substantial attention of researchers in the recent years due to its applications in numerous fields of computer vision, particularly in visual surveillance and monitoring systems. Most existing gait recognition algorithms solve the problem of person identification either by constructing a human body model based on various skeletal data characteristics such as joints positioning and their orientation, or use gait features, e.g., stride length, gait patterns and other shape templates. Such approaches require the extraction of the human-body's silhouette, contour, or skeleton from the images, and therefore their performance highly depends on the silhouette segmentation accuracy. In this paper, we propose a novel gait recognition algorithm which exploits spatiotemporal motion characteristics of a person, which does not need silhouette or skeleton extraction at all. The proposed algorithm computes a set of spatiotemporal features from the video sequences and uses them to generate a codebook. Fisher vector is used to encode the motion descriptors which are classified using linear Support Vector Machine (SVM). The proposed algorithm is evaluated on three benchmark gait datasets: TUM GAID, CASIA-B, and CASIA-C. It achieved excellent results on all datasets which demonstrate the effectiveness of the proposed algorithm.

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 patient's 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 infant's (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 infant's 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.

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 patient's 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 patient's 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 patient's body region from the scene using a k-means clustering algorithm. The movement patterns of a patient's 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.

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%.