In this paper we present the results of applying a general purpose feature combination framework for tracking
to the specific task of tracking vehicles in UAV data sets. In the fusion framework used (previously presented
elsewhere1) vehicles' pixel-based features from multiple channels, specifially RGB and thermal IR, are split across
separate individual spatiogram trackers. The use of spatiograms allows embedding of some spatial information
into the models whilst also avoiding the exponential increase in computational load and memory requirements
associated with the more commonly used histogram. This tracking framework is embedded in a complete system
for detecting and tracking vehicles. The system first carries out pre-processing to ensure spatially and temporally
aligned visible spectrum and IR data prior to tracking. Vehicle detection in the initial two frames is achieved
by first compensating for camera motion, followed by frame differencing and post-processing (thresholding and
size filtering) to identify vehicle regions. Each vehicle is then described by a bounding box and this is used to
generate a set of spatiograms for each of the available data channels. The detected vehicle is then tracked using
the spatiogram tracker framework. Results of experiments on a variety of UAV data sets indicate the promising
performance of the overall system, even in the presence of significant illumination variation, partial and full
occlusions and significant camera motion and focus change. Results are particularly encouraging given that we
do not periodically re-initialise the detection phase and this points to the robustness of the tracking framework.
The SenseCam is a prototype device from Microsoft that facilitates automatic capture of images of a person's
life by integrating a colour camera, storage media and multiple sensors into a small wearable device. However,
efficient search methods are required to reduce the user's burden of sifting through the thousands of images that
are captured per day. In this paper, we describe experiments using colour spatiogram and block-based cross-correlation
image features in conjunction with accelerometer sensor readings to cluster a day's worth of data into
meaningful events, allowing the user to quickly browse a day's captured images. Two different low-complexity
algorithms are detailed and evaluated for SenseCam image clustering.
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