Categorization of indoor places by combining local binary pattern histograms of range and reflectance data from laser range finders

This paper presents an approach to categorize typical places in indoor environments using 3D scans provided by a laser range finder. Examples of such places are offices, laboratories, or kitchens. In our method, we combine the range and reflectance data from the laser scan for the final categorization of places. Range and reflectance images are transformed into histograms of local binary patterns and combined into a single feature vector. This vector is later classified using support vector machines. The results of the presented experiments demonstrate the capability of our technique to categorize indoor places with high accuracy. We also show that the combination of range and reflectance information improves the final categorization results in comparison with a single modality

VIVA-uOttawa / CBSA at TRECVID 2012: Interactive Surveillance Event Detection ∗

We present an interactive video event detection system for the TRECVID 2012 Surveillance Event Detection (SED) task [16]. Inspired by previous TRECVID submissions, the underlying approach is built on combining automated detection of temporal regions of interest through the extraction of binary spatio-temporal keypoint descriptors in observed video-sequences (Video Analytics module), and efficient manual filtering of false alarms through the use of a custom-designed graphical user interface (Visual Analytics module). We make the automated detection of temporal regions of interest feasible by using efficient binary feature descriptors. These descriptors allow for descriptor matching in the bag-of-words model to be orders of magnitude faster than traditional descriptors, such as SIFT and optical flow. The approach is evaluated on a single task, PersonRuns, as defined by the TRECVID 2012 guidelines. The combination of Visual Analytics and Video Analytics tools is shown to be essential for the success of a highly challenging task of detecting events of interest in unstructured environments using video surveillance cameras. ∗ This work is done within the CBSA-led PSTP BTS-402 project PROVE-IT(VA) funded by the Defence Research and Developmen