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Joon Hee Han

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2 papers

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AAAI Conference 2016 Conference Paper

Unsupervised Co-Activity Detection from Multiple Videos Using Absorbing Markov Chain

Donghun Yeo
Bohyung Han
Joon Hee Han

We propose a simple but effective unsupervised learning algorithm to detect a common activity (co-activity) from a set of videos, which is formulated using absorbing Markov chain in a principled way. In our algorithm, a complete multipartite graph is ﬁrst constructed, where vertices correspond to subsequences extracted from videos using a temporal sliding window and edges connect between the vertices originated from different videos; the weight of an edge is proportional to the similarity between the features of two end vertices. Then, we extend the graph structure by adding edges between temporally overlapped subsequences in a video to handle variable-length co-activities using temporal locality, and create an absorbing vertex connected from all other nodes. The proposed algorithm identiﬁes a subset of subsequences as co-activity by estimating absorption time in the constructed graph efﬁciently. The great advantage of our algorithm lies in the properties that it can handle more than two videos naturally and identify multiple instances of a co-activity with variable lengths in a video. Our algorithm is evaluated intensively in a challenging dataset and illustrates outstanding performance quantitatively and qualitatively.

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NeurIPS Conference 2014 Conference Paper

Local Decorrelation For Improved Pedestrian Detection

Woonhyun Nam
Piotr Dollar
Joon Hee Han

Even with the advent of more sophisticated, data-hungry methods, boosted decision trees remain extraordinarily successful for fast rigid object detection, achieving top accuracy on numerous datasets. While effective, most boosted detectors use decision trees with orthogonal (single feature) splits, and the topology of the resulting decision boundary may not be well matched to the natural topology of the data. Given highly correlated data, decision trees with oblique (multiple feature) splits can be effective. Use of oblique splits, however, comes at considerable computational expense. Inspired by recent work on discriminative decorrelation of HOG features, we instead propose an efficient feature transform that removes correlations in local neighborhoods. The result is an overcomplete but locally decorrelated representation ideally suited for use with orthogonal decision trees. In fact, orthogonal trees with our locally decorrelated features outperform oblique trees trained over the original features at a fraction of the computational cost. The overall improvement in accuracy is dramatic: on the Caltech Pedestrian Dataset, we reduce false positives nearly tenfold over the previous state-of-the-art.

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