Abstract: The following paper describes the video database tool used by the Fédération Internationale de Football Association (FIFA) as part of the research project developed in collaboration with the Carlos III University of Madrid. The database project began in 2012, with the aim of creating an educational tool for the training of instructors, referees and assistant referees, and it has been used in all FUTURO III courses since 2013. The platform now contains 3,135 video clips of different match situations from FIFA competitions. It has 1,835 users (FIFA instructors, referees and assistant referees). In this work, the main features of the database are described, such as the use of a search tool and the creation of multimedia presentations and video quizzes. The database has been developed in MySQL, ActionScript, Ruby on Rails and HTML. This tool has been rated by users as "very good" in all courses, which prompt us to introduce it as an ideal tool for any other sport that requires the use of video analysis.
Abstract: In this paper, we propose a novel fast search algorithm for short MPEG video clips from video database. This algorithm is based on the adjacent pixel intensity difference quantization (APIDQ) algorithm, which had been reliably applied to human face recognition previously. An APIDQ histogram is utilized as the feature vector of the frame image. Instead of fully decompressed video frames, partially decoded data, namely DC images are utilized. Combined with active search [4], a temporal pruning algorithm, fast and robust video search can be realized. The proposed search algorithm has been evaluated by 6 hours of video to search for given 200 MPEG video clips which each length is 15 seconds. Experimental results show the proposed algorithm can detect the similar video clip in merely 80ms, and Equal Error Rate (ERR) of 3 % is achieved, which is more accurately and robust than conventional fast video search algorithm.
Abstract: Organization of video databases is becoming difficult
task as the amount of video content increases. Video classification
based on the content of videos can significantly increase the speed of
tasks such as browsing and searching for a particular video in a
database. In this paper, a content-based videos classification system
for the classes indoor and outdoor is presented. The system is
intended to be used on a mobile platform with modest resources. The
algorithm makes use of the temporal redundancy in videos, which
allows using an uncomplicated classification model while still
achieving reasonable accuracy. The training and evaluation was done
on a video database of 443 videos downloaded from a video sharing
service. A total accuracy of 87.36% was achieved.
Abstract: In this paper, we present an improved fast and robust
search algorithm for copy detection using histogram-based features for
short MPEG video clips from large video database. There are two
types of histogram features used to generate more robust features. The
first one is based on the adjacent pixel intensity difference quantization
(APIDQ) algorithm, which had been reliably applied to human face
recognition previously. An APIDQ histogram is utilized as the feature
vector of the frame image. Another one is ordinal histogram feature
which is robust to color distortion. Furthermore, by Combining with a
temporal division method, the spatial and temporal features of the
video sequence are integrated to realize fast and robust video search
for copy detection. Experimental results show the proposed algorithm
can detect the similar video clip more accurately and robust than
conventional fast video search algorithm.
Abstract: In this paper, we propose an improved fast search
algorithm using combined histogram features and temporal division
method for short MPEG video clips from large video database. There
are two types of histogram features used to generate more robust
features. The first one is based on the adjacent pixel intensity
difference quantization (APIDQ) algorithm, which had been reliably
applied to human face recognition previously. An APIDQ histogram is
utilized as the feature vector of the frame image. Another one is
ordinal feature which is robust to color distortion. Combined with
active search [4], a temporal pruning algorithm, fast and robust video
search can be realized. The proposed search algorithm has been
evaluated by 6 hours of video to search for given 200 MPEG video
clips which each length is 30 seconds. Experimental results show the
proposed algorithm can detect the similar video clip in merely 120ms,
and Equal Error Rate (ERR) of 1% is achieved, which is more
accurately and robust than conventional fast video search algorithm.