Real-Time Detection of Abandoned Object using Centroid Difference Method

An abandoned object is one that remains stationary for an extended period. Such object might contain explosives and if left on purpose could cause death and injuries to people especially in crowded places. Abandoned objects need to be detected on time to prevent what might endanger people’s lives and health. Various methods have been developed to detect abandoned objects. The most reliable one is the vision-based method which automatically detects the abandoned object using image processing. The efficiency of the method was tested and evaluated on the customized datasets as well as the i-Lids advanced video surveillance system database. The Self -organizing Background Subtraction (SOBS) method overrides other methods in terms of its detection accuracy and simplicity of implementation, but fails for dynamic background scenarios. This work presents a real time vision-based object detection method using the centroid difference to improve on the accuracy of the detection and to tackle challenges of dynamic background of the SOBS method. Matlab Image processing toolbox was used to achieve this goal. The strategy is basically decomposed into two; foreground detection and stationary foreground object (SFO) detection. Gaussian Mixture Model is used for detecting the presence of newly introduced object into a scene (foreground detection), while the blob tracking approach based on frame counting is used to determine whether the detected foreground object is static/ abandoned or not. The results show that the detection accuracy of 83% was obtained which outperform the SOBS method with 67% accuracy. Future research should focus on tracking the person that abandoned the object for onward prosecution

Illumination invariant stationary object detection

A real-time system for the detection and tracking of moving objects that becomes stationary in a restricted zone. A new pixel classification method based on the segmentation history image is used to identify stationary objects in the scene. These objects are then tracked using a novel adaptive edge orientation-based tracking method. Experimental results have shown that the tracking technique gives more than a 95% detection success rate, even if objects are partially occluded. The tracking results, together with the historic edge maps, are analysed to remove objects that are no longer stationary or are falsely identified as foreground regions because of sudden changes in the illumination conditions. The technique has been tested on over 7 h of video recorded at different locations and time of day, both outdoors and indoors. The results obtained are compared with other available state-of-the-art methods

Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network

Drone systems have been deployed by various law enforcement agencies to monitor hostiles, spy on foreign drug cartels, conduct border control operations, etc. This paper introduces a real-time drone surveillance system to identify violent individuals in public areas. The system first uses the Feature Pyramid Network to detect humans from aerial images. The image region with the human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network for human pose estimation. The orientations between the limbs of the estimated pose are next used to identify the violent individuals. The proposed deep network can learn meaningful representations quickly using ScatterNet and structural priors with relatively fewer labeled examples. The system detects the violent individuals in real-time by processing the drone images in the cloud. This research also introduces the aerial violent individual dataset used for training the deep network which hopefully may encourage researchers interested in using deep learning for aerial surveillance. The pose estimation and violent individuals identification performance is compared with the state-of-the-art techniques.Comment: To Appear in the Efficient Deep Learning for Computer Vision (ECV) workshop at IEEE Computer Vision and Pattern Recognition (CVPR) 2018. Youtube demo at this: https://www.youtube.com/watch?v=zYypJPJipY

Human behavioural analysis with self-organizing map for ambient assisted living

This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints

An improved background segmentation method for ghost removals

With ongoing research assessment in higher education and the introduction of master’s‐level work in initial teacher education, the growing need for teacher educators to develop research identities is discussed in relation to mentoring and support in two universities. Twelve interviews—with three teacher educators and three research mentors from each university—were carried out, in order to identify effective mentoring practices and other forms of support, as well as any barriers or problems encountered in developing a research profile. An innovative aspect of the methodological approach is that beginning researchers from the teacher education faculty in both universities undertook the interviewing and co‐authored the article. The need for an entitlement to and protection of research time is stressed, as well as a range of supportive practices within an active research culture. It is argued that this aspect of teacher educators’ professional development requires as much attention as the pedagogical aspects of their rol