Video tracking is a fundamental problem in computer vision with many applications. The goal of video tracking
is to isolate a target object from its background across a sequence of frames. Tracking is inherently a three
dimensional problem in that it incorporates the time dimension. As such, the computational efficiency of video
segmentation is a major challenge. In this paper we present a generic and robust graph-theory-based tracking
scheme in videos. Unlike previous graph-based tracking methods, the suggested approach treats motion as a
pixel's property (like color or position) rather than as consistency constraints (i.e., the location of the object in
the current frame is constrained to appear around its location in the previous frame shifted by the estimated
motion) and solves the tracking problem optimally (i.e., neither heuristics nor approximations are applied).
The suggested scheme is so robust that it allows for incorporating the computationally cheaper MPEG-4
motion estimation schemes. Although block matching techniques generate noisy and coarse motion fields, their
use allows faster computation times as broad variety of off-the-shelf software and hardware components that
specialize in performing this task are available. The evaluation of the method on standard and non-standard
benchmark videos shows that the suggested tracking algorithm can support a fast and accurate video tracking,
thus making it amenable to real-time applications.
Size and concentration of airborne particulate matter (PM) are important indicators of air pollution events and public health risks. It is therefore important to monitor size resolved PM concentrations in the ambient air. This task, however, is hindered by the highly dynamic spatiotemporal variations of the PM concentrations. Satellite remote sensing is a common approach for gathering spatiotemporal data regarding aerosol events but its current spatial resolution is limited to a large grid that does not fit high varying urban areas. Moreover, satellite-borne remote sensing has limited revisit periods and it measures along vertical atmospheric columns. Thus, linking satellite-borne aerosol products to ground PM measurements is extremely challenging. In the last two decades visibility analysis is used by the US Environmental Protection Agency (US-EPA) to obtain quantitative representation of air quality in rural areas by horizontal imaging. However, significantly fewer efforts have been given to utilize the acquired scene characteristics (color, contrast, etc.) for quantitative parametric modeling of PM concentrations. We suggest utilizing the image effective bandwidth, a quantitative measure of image characteristics, for predicting PM concentrations. For validating the suggested method, we have assembled a large dataset that consists of time series imaging as well as measurements from air quality monitoring stations located in the study area that report PM concentrations and meteorological data (wind direction and velocity, relative humidity, etc.). Quantitative and qualitative statistical evaluation of the suggested method shows that dynamic changes of PM concentrations can be inferred from the acquired images.
Conference Committee Involvement (5)
Real-Time Image and Video Processing 2018
16 April 2018 | Orlando, Florida, United States
Real-Time Image and Video Processing 2017
10 April 2017 | Anaheim, California, United States
Real-Time Image and Video Processing
7 April 2016 | Brussels, Belgium
Real-Time Image and Video Processing 2015
10 February 2015 | San Francisco, California, United States