Hasan Timucin Ozdemir

Top-Eye: top-k evolving trajectory outlier detection

The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for identifying abnormal moving activities. Indeed, various aspects of abnormality of moving patterns have recently been exploited, such as wrong direction and wandering. However, there is no recognized way of combining different aspects into an unified evolving abnormality score which has the ability to capture the evolving nature of abnormal moving trajectories. To that end, in this paper, we provide an evolving trajectory outlier detection method, named TOP-EYE, which continuously computes the outlying score for each trajectory in an accumulating way. Specifically, in TOP-EYE, we introduce a decay function to mitigate the influence of the past trajectories on the evolving outlying score, which is defined based on the evolving moving direction and density of trajectories. This decay function enables the evolving computation of accumulated outlying scores along the trajectories. An advantage of TOP-EYE is to identify evolving outliers at very early stage with relatively low false alarm rate. Finally, experimental results on real-world location traces show that TOP-EYE can effectively capture evolving abnormal trajectories.

A Novel 2D Marker Design and Application for Object Tracking and Event Detection

In this paper we present a novel application which uses 2D barcode for object tracking and event detection. We analyze the relationship between the spatial efficiency of a marker and its robustness against defocusing. Based on our analysis we design a spatially efficient and robust 2D barcode, M-Code (Marker-Code) which can be attached to the surface of static or moving objects. Compared with traditional object detection and tracking methods, M-Code not only identifies and tracks an object but also reflects its position and the orientation of the surface where it is attached. We implemented an efficient algorithm that tracks multiple M-Codes in real time in the presence of rotation and perspective distortion. In experiments we compare the spatial efficiency of M-Code with existing 2D barcodes, and quantitatively measure its robustness, including its scaling capability and tolerance to perspective distortion. As an application we use the system to detect door movements and track multiple moving objects in real scenes.

Direction clustering for characterizing movement patterns

The increasing availability of motion data creates unprecedent opportunities to change the paradigm for characterizing movement patterns. While cluster analysis is usually a useful starting point for understanding and exploring data, conventional clustering algorithms are not designed for handling trajectory data. Therefore, in this paper, we propose a direction-based clustering (DEN) method, which aims to group trajectories by moving directions. A key development challenge is how to transform direction information into a data format which is appropriate for traditional clustering algorithms to explore. To this end, we partition the space into grids and turn the movement statistics in a grid into a vector which represents the probabilities of moving directions within the grid. With such data transformation, we are able to develop a grid-level K-means clustering method for direction clustering. We illustrate the use of DEN for showing movement patterns and detecting outliers on real-world data sets.