Department of Applied Mathematics

Permanent URI for this community

https://scholar.sun.ac.za/handle/10019.1/96341

Browse

Browsing Department of Applied Mathematics by Subject "Automatic tracking"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Thermal and colour data fusion for people detection and tracking
    (Stellenbosch : Stellenbosch University, 2014-04) Joubert, Pierre; Brink, Willie; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences.
    ENGLISH ABSTRACT: In this thesiswe approach the problem of tracking multiple people individually in a video sequence. Automatic object detection and tracking is non-trivial as humans have complex and mostly unpredictable movements, and there are sensor noise and measurement uncertainties present. We consider traditional object detection methods and decide to use thermal data for the detection step. This choice is supported by the robustness of thermal data compared to colour data in unfavourable lighting conditions and in surveillance applications. A drawback of using thermal data is that we lose colour information, since the sensor interprets the heat emission of the body rather than visible light. We incorporate a colour sensor which is used to build features for each detected object. These features are used to help determine correspondences in detected objects over time. A problem with traditional blob detection algorithms, which typically consist of background subtraction followed by connected-component labelling, is that objects can appear to split or merge, or disappear in a few frames. We decide to add ‘dummy’ blobs in an effort to counteract these problems. We refrain from making any hard decisions with respect to the blob correspondences over time, and rather let the system decide which correspondences are more probable. Furthermore, we find that the traditional Markovian approach of determining correspondences between detected blobs in the current time step and only the previous time step can lead to unwanted behaviour. We rather consider a sequence of time steps and optimize the tracking across them. We build a composite correspondence model and weigh each correspondence according to similarity (correlation) in object features. All possible tracks are determined through this model and a likelihood is calculated for each. Using the best scoring tracks we then label all the detections and use this labelling as measurement input for a tracking filter. We find that the window tracking approach shows promise even though the data we us for testing is of poor quality and noisy. The system struggles with cluttered scenes and when a lot of dummy nodes are present. Nonetheless our findings act as a proof of concept and we discuss a few future improvements that can be considered.