Multimedia Event Detection Using Segment-Based Approach for Motion Feature

Sang Phan,Duy-Dinh Le,Thanh Duc Ngo,Son Tran,Vu Lam,Shin’Ichi Satoh,Duc Anh Duong

doi:10.1007/s11265-013-0825-4

Abstract

Multimedia event detection has become a popular research topic due to the explosive growth of video data. The motion features in a video are often used to detect events because an event may contain some specific actions or moving patterns. Raw motion features are extracted from the entire video first and then aggregated to form the final video representation. However, this video-based representation approach is ineffective when used for realistic videos because the video length can be very different and the clues for determining an event may happen in only a small segment of the entire video. In this paper, we propose using a segment-based approach for video representation. Basically, original videos are divided into segments for feature extraction and classification, while still keeping the evaluation at the video level. The experimental results on recent TRECVID Multimedia Event Detection datasets proved the effectiveness of our approach.

Highlights

Multimedia Event Detection (MED) is a challenging task in TREC Video Retrieval Evaluation (TRECVID).1 The task is defined as follow: given a collection of test videos and a list of test events, indicate whether each of the test events is present in each of the test videos
We present the results of combining various segment lengths using the late fusion technique
We report the performance of our baseline event detection system using the keyframe-based and video-based approach for comparison

Summary

Introduction

Multimedia Event Detection (MED) is a challenging task in TREC Video Retrieval Evaluation (TRECVID). The task is defined as follow: given a collection of test videos and a list of test events, indicate whether each of the test events is present in each of the test videos. Multimedia Event Detection (MED) is a challenging task in TREC Video Retrieval Evaluation (TRECVID).. The task is defined as follow: given a collection of test videos and a list of test events, indicate whether each of the test events is present in each of the test videos. The aim of MED is to develop systems that can automatically find video containing any event of interest, assuming only a limited number of training exemplars are given. The need for such MED systems is rising because a massive number of videos are produced every day.

Methods

Results

Conclusion