Let t > 0. Recall that EM∗(t) = α(t) − tλ(t) where α(t) denote the mass at level t, namely α(t) = P(f(X) ≥ t), and λ(t) denote the volume at level t, i.e. λ(t) = Leb({x, f(x) ≥ t}). For h > 0, let A(h) denote the quantity A(h) = 1/h(α(t + h) − α(t)) and B(h) = 1/h(λ(t + h) − λ(t)). It is straightforward to see that A(h) and B(h) converge when h→ 0, and expressing EM∗ ′ = α′(t)−tλ′(t)−λ(t), it s...

In this paper, we present a novel anomaly detection framework which integrates motion and appearance cues to detect abnormal objects and behaviors in video. For motion anomaly detection, we employ statistical histograms to model the normal motion distributions and propose a notion of “cut-bin” in histograms to distinguish unusual motions. For appearance anomaly detection, we develop a novel sch...

While network-wide methods have become popular in the anomaly detection literature, there has been no quantitative evaluation of the advantage of such methods. In this paper we provide preliminary results of this analysis. Surprisingly, we observe that most of the anomalies found by a network-wide method are also found by a simpler single-link approach.

We propose a novel probabilistic semi-supervised anomaly detection framework for multi-dimensional systems with high correlation among variables. Our method is able to identify both abnormal instances and abnormal variables of an instance.

Monday December 7 08:00-09:00 Registration (3rd Floor Grand Ballroom, GRAND GONGDA JIANGUO HOTEL of Beijing University of Technology) 09:00-09:15 Open remarks 09:15-10:05 Keynote 1 Robert Deng (Singapore Management University) 10:05-10:55 Keynote 2 Wenchang Shi (Renmin University) 10:55-11:15 Tea & Coffee Break 11:15-12:05 Keynote 3 Rob Spiger (Microsoft) 12:05-13:30 Lunch 13:30-15:00 Session 1...

MODELING LOCAL VIDEO STATISTICS FOR ANOMALY DETECTION