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10 years of news and resources for members of the IEEE Signal Processing Society
The signal processing cup, called the SP Cup, is an annual competition in the IEEE ICASSP conference. The goal of this competition is to encourage teams of 3-10 undergraduate students to solve real world problems using tools from signal processing. Teams that secure the first, second, and third spot win a prize money of $5000, $2500, and $1500. This year’s SP Cup was sponsored by Mathworks Inc. Interested to participate in the next year’s SP Cup? Let’s hear from the winning team of SP Cup 2020.
The Winner: A team of five people from the Signal and Image Processing Lab (SIPL) at Technion - Israel Institute of Technology won the SP Cup 2020. This team was supervised by Yair Moshe and was tutored by Pavel Lifshits. Undergraduate students Theo Adrai, David Ben-Said, and Samuel Sendrowicz participated in this project.
Q2. Congratulations on winning the contest! We’d be happy to hear about your solution on anomaly detection.
Most of the other teams have proposed solutions based on deep neural networks. Such solutions have recently become very popular, but they have several drawbacks, especially when dealing with a small amount of data, such as in this competition. Our solution stands out as it takes a different approach. We embed the noisy high-dimensional multimodal data in a low dimension representation and utilize the correlation between sensors as an indicator of abnormality. First, appearance features are extracted from each image by a pre-trained ResNet-18 deep neural network. Then, appearance and motion features are fused into a multivariate time series. Each time window is embedded in a known cone manifold, using a Gaussian kernel function. The resulting kernel matrix captures the pairwise similarity between each pair of windows. We then exploit the geometric properties of the reduced dimension manifold and use its intrinsic distance as a measure of abnormality. The proposed method is attractive as it is fully unsupervised, data agnostic, and noise-robust. As an example, we have shown that our solution can not only detect abnormal events of different types in the competition dataset, but also in another inherently different dataset.
Block diagram of the anomaly detection technique by Yair, Pavel and their team.
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