Improved abnormal detection using self-adaptive social force model for visual surveillance

With the growth of technology in computer vision, there is a great demand for an automated surveillance system in replaced to the traditional visual surveillance. The automated surveillance system is a system that monitors the behavior and activities of the crowd whether it is normal or not. The abnormal detection in a crowd is a noteworthy research topic in automated surveillance system in public places. It is emergent to detect the abnormal events as quickly as possible and take appropriate actions to minimize the loss and ensure the public safety. In this work, we aim to find the significant interaction forces and detect the abnormality in the crowd by using Self-Adaptive Social Force Model. For this point, Horn-Schunck optical flow is used to get the flow vector for each pixel in the image frames. Instead of tracking individuals, particle advection is performed to capture continuity of crowd flow and its trajectories. These particles are then advected to a new location according to its underlying optical flow vector at the current location. Using the attained flow vectors from this stage, interaction force estimation is done based on SFM theory. This experiment is done with the hypothesis that high magnitude of interaction force portrayed the abnormal behavior in a crowd. However, there is a problem with the earlier SFM, which is the similarity of actual velocity and desired velocity caused the abnormal detection inaccurate. The estimation of the good quality of interaction forces is critical in this case and has not been explored yet. So, Self-Adaptive SFM is developed in order to estimate a good quality of interaction forces since it is crucial to achieve better abnormal detection, which represents the behavior of the crowd. From the experiment, the highest and least magnitude of interaction force can be localized in the image frame. The proposed algorithm is validated with three challenging datasets contain abnormal videos, including the videos of crime in Malaysia. For both indoor and outdoor scene, the proposed algorithm outperforms the other methods with accuracy 97% and 100%. For the benchmarking datasets, the AUC (Area under Curve) score of the proposed algorithm is quite comparable with previous works with the score of 0.9916. The AUC score provided by the proposed algorithm on PETS2009 datasets is about 0.9026 and 0.9940 for Malaysia Crime dataset. Based on these results, it can conclude that the high magnitudes of interaction forces portray the abnormality in the scene and Self-Adaptive SFM is well-performed on crime scene with the rapid motion characteristic

AC motor position control using fuzzy logic controller

Motor position control is very important in rotating machinery applications. There are many applications that have been developed based on motor position control theory, such as crane controller, lift and conveyor. The position control of an ac motor is very difficult to be implemented by using traditional control techniques, as it requires a very complex mathematical model. The purpose for this project is to describe the research on fuzzy logic controller (FLC) design based on programmable logic controller (PLC) in order to control the position of an ac motor of University Malaysia Pahang (UMP) mini conveyor. By using FLC, the conveyor will stop at the desired point set by the user with minimum error. The model of the PLC that has been used in this project is OMRON CQM1H-CPU51. Before the controller was developed, numbers of simulations were done using MATLAB Fuzzy Logic Toolbox and SIMULINK. There are three rules that have been implemented in this project, which used three membership functions. Based on the simulation, it can be concluded that the system which has many rules in the fuzzy logic controller produced better response compared to the system using a few rules

Motion detection using Horn-Schunck optical flow

This system is design to detect motion in a crowd using one of the optical flow algorithms, Horn-Schunck method. By performing some appropriate feature extraction techniques, this system allows us to achieve better results in detecting motion and determining the velocity of that motion in order to analyze the human behaviour based on its velocities. This research works able to help human observer to monitor video recorded by closed-circuit television systems (CCTVs) attached in the region of interest (ROI) area

ABNORMALITY DETECTION AND LOCALIZATION USING MODIFIED SFM

ABSTRACT Social Force Model (SFM) is commonly used in crowd analysis. In this paper, modified SFM is proposed to detect and localize the abnormality in crowd scene. This task is done by estimating the interaction forces in image frames based on SFM theory. The algorithm is jointly used with optical flow, which provides flow vector to be used in particle advection. The moving particles are treated as a main cue instead of particle tracking. Some modifications of SFM algorithm has been proposed here in order to capture the particles which carry significant information of the crowd. The interaction forces are being selected based on Fisher's equation. The computed interaction forces determine the synergy between the advected particles, whereby high magnitude of interaction force has high possibility of abnormal behaviour happened

Crowd behavior monitoring using self-adaptive social force model

Crowd can be defined as a large number of people gathered closely together. The larger size of crowd results number of behavior either in group or individually. To prevent or minimize the effects of the abnormal behavior, it has to be monitored continuously. Crowd behavior monitoring is an important task in public places to ensure public safety and avoid any unwanted incidents. It has become a popular research among computer vision communities nowadays due to its needs by the authorities. The current method used is Social Force Model (SFM), which can describe the behavior of a crowd based on the interaction forces between individuals. However, some limitations in the previous works caused by its parameters make it fail to correctly classify the crowd behavior into normal or abnormal. Hence, some modification has been introduced to SFM theory in order to provide significant interaction force; which absolutely portrayed the behavior of the crowd. This work aims to develop a crowd behavior monitoring system using Self-Adaptive SFM. This algorithm is jointly used with Horn-Schunck optical flow as a motion detector for the input video. Instead of using any segmentation methods, the motion of particles in each frame is captured by particle advection method. This is done by advected the particles using the underlying flow vectors of each particle. The obtained new locations for all the particles are necessary in estimating the interaction force of each particle. The combination of psychological and physical parameters in Self-Adaptive SFM makes it more realistic and mimicked the dynamic motion of people in a crowd. The estimated interaction forces of each particle represent the behavior of the crowd, whether it is normal or abnormal. The experimental evaluations on challenging datasets shows that the proposed method achieves the better detection result and outperforms the other methods, optical flow and SFM; with the average accuracy of about 94%

Analysis of Motion Detection using Social Force Model

Crowd behaviour detection is becoming a significant research topic in surveillance system in public places. This paper presents a method for the detection of abnormality in crowded scenes based on Social Force Model. For this purpose, Horn-Schunck optical flow is used in order to find the flow vector for all video frames. Using the vectors from this method, the interaction forces for each particle in video frames is calculated based on Social Force Model algorithm. The abnormal and normal frames are then classified by using a bag of words approach, whereby the region of anomalies in the abnormal frames are localized using interaction forces obtained in the previous experiment

Sunglass detection method for automation of video surveillance system

Wearing sunglass to hide face from surveillance camera is a common activity in criminal incidences. Therefore, sunglass detection from surveillance video has become a demanding issue in automation of security systems. In this paper we propose an image processing method to detect sunglass from surveillance images. Specifically, a unique feature using facial height and width has been employed to identify the covered region of the face. The presence of covered area by sunglass is evaluated using facial height-width ratio. Threshold value of covered area percentage is used to classify the glass wearing face. Two different types of glasses have been considered i.e. eye glass and sunglass. The results of this study demonstrate that the proposed method is able to detect sunglasses in two different illumination conditions such as, room illumination as well as in the presence of sunlight. In addition, due to the multi-level checking in facial region, this method has 100% accuracy of detecting sunglass. However, in an exceptional case where fabric surrounding the face has similar color as skin, the correct detection rate was found 93.33% for eye glass

Camera-projector calibration for near infrared imaging system

Advanced biomedical engineering technologies are continuously changing the medical practices to improve medical care for patients. Needle insertion navigation during intravenous catheterization process via Near infrared (NIR) and camera-projector is one solution. However, the central point of the problem is the image captured by camera misaligns with the image projected back on the object of interest. This causes the projected image not to be overlaid perfectly in the real-world. In this paper, a camera-projector calibration method is presented. Polynomial algorithm was used to remove the barrel distortion in captured images. Scaling and translation transformations are used to correct the geometric distortions introduced in the image acquisition process. Discrepancies in the captured and projected images are assessed. The accuracy of the image and the projected image is 90.643%. This indicates the feasibility of the captured approach to eliminate discrepancies in the projection and navigation images

Triangle and trapezoid area features for gait authentication

This paper presents two gait authentication features based on geometric shape for gait analysis. Specifically, triangle and trapezoid based features are proposed for gait authentication. The features are based on the geometric pattern extracted from a particular gait cycle of a gait model. These features use four points from hip-knee-toe joints and construct a triangle and a right trapezoid. The area of the triangle and trapezoid are calculated using geometric formula as well as image processing methods. Later two areas are compared to validate the model free approach. The results show that, the proposed feature can be used as the features in model free gait analysis

Ultra wide Band (UWB) Based Early Breast Cancer Detection using Artificial Intelligence

Breast cancer is a silent killer malady among women community all over the world. The death rate is increased as it has no syndrome at early stage. There is no remedy; hence, detection at the early stage is crucial. Usually, women do not go to clinic/hospital for regular breast health checkup unless they are sick. This is due to long queue and waiting time in hospital, high cost, people‟s busy schedule, and so on. Recently, several research works has been done on early breast cancer detection using Ultra Wide Band (UWB) technolo-gy because of its non-invasive and health-friendly nature. Each proposed UWB system has its own limitation including system complexity, expensive, expert operable in clinic. To overcome these problems, a system is required which should be simple, cost-effective and user-friendly. This chapter presents the de-velopment of a user friendly and affordable UWB system for early breast can-cer detection utilizing Artificial Neural Network (ANN). A feed-forward back propagation Neural Network (NN) with 'feedforwardnet' function is utilized to detect the cancer existence, size as well as location in 3-dimension (3D). The hardware incorporates UWB transceiver and a pair of pyramidal shaped patch antenna to transmit and receive the UWB signals. The extracted features from the received signals have been fed into the NN module to train, validate, and test. The average system‟s performance efficiency in terms of tumor/cancer ex-istence, size and location are approximately 100%, 92.43% and 91.31 % respec-tively. Here, in our system, use of „feedforwardnet‟ function; detection-combination of tumor/cancer existence, size and location in 3D along with im-proved performance is a new addition compared to other related researches and/or existing systems. This may become as a promising user-friendly system in near future for early breast cancer detection in domestic environment with low cost and to save precious life