Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures
By Willie Neiswanger, Frank D. Wood. 2012
3 citations recorded by the source corpus.
This paper proposes a technique for the unsupervised detection and tracking of arbitrary objects in videos. It is intended to reduce the need for detection and localization methods tailored to specific object types and serve as a general framework applicable to videos with varied objects, backgrounds, and image qualities. The technique uses a dependent Dirichlet process mixture (DDPM) known as the Generalized Polya Urn (GPUDDPM) to model image pixel data that can be easily and efficiently extracted from the regions in a video that represent objects. This paper describes a specific implementation of the model using spatial and color pixel data extracted via frame differencing and gives two algorithms for performing inference in the model to accomplish detection and tracking. This technique is demonstrated on multiple synthetic and benchmark video datasets that illustrate its ability to, without modification, detect and track objects with diverse physical characteristics moving over non-uniform backgrounds and through occlusion.
Subfields: Artificial Intelligence, Biomedical Engineering, Media Technology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Adam R. Kosiorek
- Yee Whye Teh
- Ingmar Posner
- Hyunjik Kim
- Sergey Levine
- Yoshua Bengio
- David M. Blei
- Michael I. Jordan
In Biomedical Engineering
In Media Technology
Citations in the corpus
- Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects (2018, background): “Bayesian Nonparametric Models To the best of our knowledge, Neiswanger and Wood, 2012 is the only known approach that models pixels belonging to a variable number of objects in a video together with their locations in the generative sense.”