Paper Info
Title
Searching a specific person in a specific location using deep features.
Abstract
Video instance search or also well known as object retrieval is a fundamental task in computer vision field and has a lot of applications. Most state-of-the-art systems are based on the Bag-of-Words model (BOW) for representing video frames and target object. When searching on nearly planar and rich-textured objects such as buildings and book cover, BOW argue to be a suitable model with very high performance. However, when searching on harder but more popular objects such as a specific person, BOW model still keep a lower performance. In this paper, we consider a new type of query which covers most popular topics: searching a person in a specific location. Inspired by recent successes of deep learning techniques, we propose new framework which leverage the powerful of both BOW model and deep feature in instance search. In particular, we use a linear kernel classifier instead of using L2 distance to compute similarity between two deep features. For further improvement, scene tracking are employed to deal with the cases face of query person is not detected. To evaluate the proposed methods, we conduct experiments over a standard benchmark dataset (TRECVID Instance Search 2016) with more than 300 GB in storage and 464 hours in duration. The results show that, our proposed methods significant improve the baseline system.
Year
DOI
Venue
2016
10.1145/3011077.3011138
Proceedings of the Seventh Symposium on Information and Communication Technology
Keywords
Field
DocType
Video Instance Search, Deep Neural Network, Location Search, Person Search, Scene tracking
Kernel (linear algebra),Computer science,TRECVID,Artificial intelligence,Baseline system,Deep learning,Classifier (linguistics),Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
21
Authors
5
Name
Order
Citations
PageRank
Vinh-Tiep Nguyen12522.31
Minh-Triet Tran214359.60
Thanh Duc Ngo38222.24
Duy-dinh Le421338.89
Duc Anh Duong511219.65