Comparing the output of a physics simulation with an experiment is
often done by visually comparing the two outputs. In order to
determine which simulation is a closer match to the experiment, more
quantitative measures are needed. This paper describes our early
experiences with this problem by considering the slightly simpler
problem of finding objects in a image that are similar to a given
query object. Focusing on a dataset from a fluid mixing problem, we
report on our experiments using classification techniques from machine
learning to retrieve the objects of interest in the simulation data.
The early results reported in this paper suggest that machine learning
techniques can retrieve more objects that are similar to the query
than distance-based similarity methods.
KEYWORDS: Video, Digital filtering, RGB color model, Cameras, Video surveillance, Data modeling, Filtering (signal processing), Fiber optic gyroscopes, Visual process modeling, Algorithm development
Identifying moving objects from a video sequence is a fundamental and
critical task in many computer-vision applications. A common approach
is to perform background subtraction, which identifies moving objects
from the portion of a video frame that differs significantly from a
background model. There are many challenges in developing a good
background subtraction algorithm. First, it must be robust against
changes in illumination. Second, it should avoid detecting
non-stationary background objects such as swinging leaves, rain, snow,
and shadow cast by moving objects. Finally, its internal background
model should react quickly to changes in background such as starting
and stopping of vehicles. In this paper, we compare various background subtraction algorithms for detecting moving vehicles and pedestrians in urban traffic video sequences. We consider approaches varying from simple techniques such as frame differencing and adaptive median filtering, to more sophisticated probabilistic modeling techniques. While complicated techniques often produce superior performance, our experiments show that simple techniques such as adaptive median filtering can produce good results with much lower computational complexity.
KEYWORDS: Video, Video surveillance, Databases, Video compression, Video processing, Multimedia, Genetic algorithms, Tolerancing, Algorithm development, Internet
With ever more popularity of video web-publishing, many popular contents are being mirrored, reformatted, modified and republished, resulting in excessive content duplication. While such redundancy provides fault tolerance for continuous availability of information, it could potentially create problems for multimedia search engines in that the search results for a given query might become repetitious, and cluttered with a large number of duplicates. As such, developing techniques for detecting similarity and duplication is important to multimedia search engines. In addition, content providers might be interested in identifying duplicates of their content for legal, contractual or other business related reasons. In this paper, we propose an efficient algorithm called video signature to detect similar video sequences for large databases such as the web. The idea is to first form a 'signature' for each video sequence by selection a small number of its frames that are most similar to a number of randomly chosen seed images. Then the similarity between any tow video sequences can be reliably estimated by comparing their respective signatures. Using this method, we achieve 85 percent recall and precision ratios on a test database of 377 video sequences. As a proof of concept, we have applied our proposed algorithm to a collection of 1800 hours of video corresponding to around 45000 clips from the web. Our results indicate that, on average, every video in our collection from the web has around five similar copies.
Conference Committee Involvement (1)
Biometric and Surveillance Technology for Human and Activity Identification X
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