Tracking Missing Person in Large Crowd Gathering Using Intelligent Video Surveillance.

Locating a missing child or elderly person in a large gathering through face recognition in videos is still challenging because of various dynamic factors. In this paper, we present an intelligent mechanism for tracking missing persons in an unconstrained large gathering scenario of Al-Nabawi Mosque, Madinah, KSA. The proposed mechanism in this paper is unique in two aspects. First, there are various proposals existing in the literature that deal with face detection and recognition in high-quality images of a large crowd but none of them tested tracking of a missing person in low resolution images of a large gathering scenario. Secondly, our proposed mechanism is unique in the sense that it employs four phases: (a) report missing person online through web and mobile app based on spatio-temporal features; (b) geo fence set estimation for reducing search space; (c) face detection using the fusion of Viola Jones cascades LBP, CART, and HAAR to optimize the results of the localization of face regions; and (d) face recognition to find a missing person based on the profile image of reported missing person. The overall results of our proposed intelligent tracking mechanism suggest good performance when tested on a challenging dataset of 2208 low resolution images of large crowd gathering.

A Novel Integration of Face-Recognition Algorithms with a Soft Voting Scheme for Efficiently Tracking Missing Person in Challenging Large-Gathering Scenarios.

The probability of losing vulnerable companions, such as children or older ones, in large gatherings is high, and their tracking is challenging. We proposed a novel integration of face-recognition algorithms with a soft voting scheme, which was applied, on low-resolution cropped images of detected faces, in order to locate missing persons in a challenging large-crowd gathering. We considered the large-crowd gathering scenarios at Al Nabvi mosque Madinah. It is a highly uncontrolled environment with a low-resolution-images data set gathered from moving cameras. The proposed model first performs real-time face-detection from camera-captured images, and then it uses the missing person's profile face image and applies well-known face-recognition algorithms for personal identification, and their predictions are further combined to obtain more mature prediction. The presence of a missing person is determined by a small set of consecutive frames. The novelty of this work lies in using several recognition algorithms in parallel and combining their predictions by a unique soft-voting scheme, which in return not only provides a mature prediction with spatio-temporal values but also mitigates the false results of individual recognition algorithms. The experimental results of our model showed reasonably good accuracy of missing person's identification in an extremely challenging large-gathering scenario.

A dataset build using wearable inertial measurement and ECG sensors for activity recognition, fall detection and basic heart anomaly detection system.

This paper defines two major data sets 1) from wearable inertial measurement sensors and 2) wearable ECG SHIMMER™ sensors. The first dataset is devised to benchmark techniques dealing with human behavior analysis based on multimodal inertial measurement wearable SHIMMER™ sensors unit during research studies "Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data" [2] and "A novel fall detection algorithm for elderly using SHIMMER wearable sensors" [3]. The SHIMMER inertial sensor is a lightweight sensing device, incorporated with tri-axial accelerometer, a tri-axial gyroscope and tri-axial magnetometer, mounted on the waist of the subjects. The second dataset is developed to assess the feasibility of using SHIMMER™ wearable third generation ECG sensors for identification of basic heart anomalies by remote ECG analysis. The experimental protocol was carried out according to the Timed Up and Go (TUG) test [1], which is mainly used in fall detection and fall risk assessment systems specially designed for elderly. Three daily life activities such as standing still, walking and sitting on chair and getup were performed along with fall activity in controlled environment. This dataset is available on Data in Brief Dataverse [4] and a data repository [5].

Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data.

OBJECTIVES: Falling in the elderly is considered a major cause of death. In recent years, ambient and wireless sensor platforms have been extensively used in developed countries for the detection of falls in the elderly. However, we believe extra efforts are required to address this issue in developing countries, such as Pakistan, where most deaths due to falls are not even reported. Considering this, in this paper, we propose a fall detection system prototype that s based on the classification on real time shimmer sensor data. METHODS: We first developed a data set, 'SMotion' of certain postures that could lead to falls in the elderly by using a body area network of Shimmer sensors and categorized the items in this data set into age and weight groups. We developed a feature selection and classification system using three classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN). Finally, a prototype was fabricated to generate alerts to caregivers, health experts, or emergency services in case of fall. RESULTS: To evaluate the proposed system, SVM, KNN, and NN were used. The results of this study identified KNN as the most accurate classifier with maximum accuracy of 96% for age groups and 93% for weight groups. CONCLUSIONS: In this paper, a classification-based fall detection system is proposed. For this purpose, the SMotion data set was developed and categorized into two groups (age and weight groups). The proposed fall detection system for the elderly is implemented through a body area sensor network using third-generation sensors. The evaluation results demonstrate the reasonable performance of the proposed fall detection prototype system in the tested scenarios.