Discovering objects and their location in videos using spatial-temporal context words

Hao Sun; Cheng Wang; Boliang Wang; Naser El-Sheimy

doi:10.1117/1.3488041

Abstract

We present a novel unsupervised learning algorithm for discovering objects and their location in videos from moving cameras. The videos can switch between different shots, and contain cluttered background, occlusion, camera motion, and multiple independently moving objects. We exploit both appearance consistency and spatial configuration consistency of local patches across frames for object recognition and localization. The contributions of this paper are twofold. First, we propose a combined approach for simultaneous spatial context and temporal context generation. Local video patches are extracted and described using the generated spatial-temporal context words. Second, a dynamic topic model, based on the representation of a bag of spatial-temporal context words, is introduced to learn object category models in video sequences. The proposed model can categorize and localize multiple objects in a single video. Objects leaving or entering the scene at multiple times can also be handled efficiently in the dynamic framework. Experimental results on the CamVid data set and the VISAT™ data set demonstrate the effectiveness and robustness of the proposed method.

Citation Download Citation

Hao Sun, Hao Sun, Cheng Wang, Cheng Wang, Boliang Wang, Boliang Wang, Naser El-Sheimy, Naser El-Sheimy, } "Discovering objects and their location in videos using spatial-temporal context words," Optical Engineering 49(9), 097003 (1 September 2010). https://doi.org/10.1117/1.3488041 . Submission:

ACCESS THE FULL ARTICLE

PERSONAL SIGN IN
Full access may be available with your subscription

Email or Username Forgot your username?

Password Forgot your password?

Show

Remember Email/Username on this computer

Remember Password

No SPIE account? Create an account
or Institutional Sign In via Shibboleth

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $20.00

Non-members: $25.00 ADD TO CART

Show All Keywords

Keywords/Phrases

Search In:

Publication Years