Author name cluster

Atsushi Suzuki

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

GLoMOT: Efficient Online GNN-based Low-Frame-Rate Multi-Object Tracker

Yaxuan Hu
Jie Hua
Gang Wu
Yuhong Yang
Atsushi Suzuki
Zhongyuan Wang

Low-frame-rate (LFR) Multi-Object Tracking (MOT) is crucial for efficient tracking on edge devices, as it significantly reduces computational and storage demands. However, existing trackers struggle in LFR settings due to large temporal gaps, extreme appearance changes, and motion non-linearity. While Graph Neural Network (GNN)-based trackers are effective at associating objects across these gaps, most operate offline, which prevents their use for online tracking. To address these limitations, we propose GLoMOT, a novel online GNN-based Low-Frame-Rate Multi-Object Tracker designed for robust performance in LFR videos. To bridge the large temporal gaps, we introduce a Dynamic Node Buffer Pool. This acts as a long-term memory, caching the states of absent objects to enable their robust re-association. To tackle extreme motion uncertainty, we propose an adaptive context-aware module that dynamically adjusts the weights of positional and appearance features, generating more robust features for predicting node connections. Furthermore, we propose a pseudo-depth feature calculation method. This provides the GNN with critical geometric context, which helps resolve spatial ambiguity arising from occlusions. Extensive experiments on several public MOT benchmarks, including DanceTrack, MOT17, and VisDrone, demonstrate GLoMOT's effectiveness and superiority, particularly in challenging Low-Frame-Rate conditions.

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IROS Conference 2024 Conference Paper

Extrinsic Calibration of Multiple LiDARs for a Mobile Robot based on Floor Plane And Object Segmentation

Shun Niijima
Atsushi Suzuki
Ryoichi Tsuzaki
Masaya Kinoshita

The utilization of mobile robots equipped with multiple light detection and ranging (LiDAR) sensors, capable of perceiving their surroundings, is on the rise due to the miniaturization and cost reduction of LiDAR technology. This paper introduces a target-less extrinsic calibration method for multiple LiDARs with non-overlapping fields of view (FoV). The proposed method leverages accumulated point clouds of the floor plane and objects obtained during robot motion. It enables accurate calibration, even in challenging configurations where LiDARs are directed towards the floor plane, which can introduce biased feature values. Additionally, the method incorporates a noise removal module that takes into account the scanning pattern to address bleeding points, which are significant sources of error in point cloud alignment when using high-density LiDARs. Evaluations conducted through simulation demonstrate that the proposed method achieves higher accuracy in extrinsic calibration with two and four LiDARs compared to conventional methods, regardless of the type of objects. Furthermore, experiments conducted using a real mobile robot validate the effectiveness of our proposed noise removal module in precisely eliminating noise compared to conventional methods. The estimated extrinsic parameters successfully contribute to the creation of consistent 3D maps.

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IJCAI Conference 2022 Conference Paper

Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction

Takayuki Katsuki
Kohei Miyaguchi
Akira Koseki
Toshiya Iwamori
Ryosuke Yanagiya
Atsushi Suzuki

We address the problem of predicting when a disease will develop, i. e. , medical event time (MET), from a patient's electronic health record (EHR). The MET of non-communicable diseases like diabetes is highly correlated to cumulative health conditions, more specifically, how much time the patient spent with specific health conditions in the past. The common time-series representation is indirect in extracting such information from EHR because it focuses on detailed dependencies between values in successive observations, not cumulative information. We propose a novel data representation for EHR called cumulative stay-time representation (CTR), which directly models such cumulative health conditions. We derive a trainable construction of CTR based on neural networks that has the flexibility to fit the target data and scalability to handle high-dimensional EHR. Numerical experiments using synthetic and real-world datasets demonstrate that CTR alone achieves a high prediction performance, and it enhances the performance of existing models when combined with them.

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NeurIPS Conference 2021 Conference Paper

Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic

Atsushi Suzuki
Atsushi Nitanda
Jing Wang
Linchuan Xu
Kenji Yamanishi
Marc Cavazza

Graph embedding, which represents real-world entities in a mathematical space, has enabled numerous applications such as analyzing natural languages, social networks, biochemical networks, and knowledge bases. It has been experimentally shown that graph embedding in hyperbolic space can represent hierarchical tree-like data more effectively than embedding in linear space, owing to hyperbolic space's exponential growth property. However, since the theoretical comparison has been limited to ideal noiseless settings, the potential for the hyperbolic space's property to worsen the generalization error for practical data has not been analyzed. In this paper, we provide a generalization error bound applicable for graph embedding both in linear and hyperbolic spaces under various negative sampling settings that appear in graph embedding. Our bound states that error is polynomial and exponential with respect to the embedding space's radius in linear and hyperbolic spaces, respectively, which implies that hyperbolic space's exponential growth property worsens the error. Using our bound, we clarify the data size condition on which graph embedding in hyperbolic space can represent a tree better than in Euclidean space by discussing the bias-variance trade-off. Our bound also shows that imbalanced data distribution, which often appears in graph embedding, can worsen the error.

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IJCAI Conference 2019 Conference Paper

Attributed Subspace Clustering

Jing Wang
Linchuan Xu
Feng Tian
Atsushi Suzuki
Changqing Zhang
Kenji Yamanishi

Existing methods on representation-based subspace clustering mainly treat all features of data as a whole to learn a single self-representation and get one clustering solution. Real data however are often complex and consist of multiple attributes or sub-features, such as a face image has expressions or genders. Each attribute is distinct and complementary on depicting the data. Failing to explore attributes and capture the complementary information among them may lead to an inaccurate representation. Moreover, a single clustering solution is rather limited to depict data, which can often be interpreted from different aspects and grouped into multiple clusters according to attributes. Therefore, we propose an innovative model called attributed subspace clustering (ASC). It simultaneously learns multiple self-representations on latent representations derived from original data. By utilizing Hilbert Schmidt Independence Criterion as a co-regularizing term, ASC enforces that each self-representation is independent and corresponds to a specific attribute. A more comprehensive self-representation is then established by adding these self-representations. Experiments on several benchmark image datasets have demonstrated the effectiveness of ASC not only in terms of clustering accuracy achieved by the integrated representation, but also the diverse interpretation of data, which is beyond what current approaches can offer.

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AAAI Conference 2019 Conference Paper

Orderly Subspace Clustering

Jing Wang
Atsushi Suzuki
Linchuan Xu
Feng Tian
Liang Yang
Kenji Yamanishi

Semi-supervised representation-based subspace clustering is to partition data into their underlying subspaces by finding effective data representations with partial supervisions. Essentially, an effective and accurate representation should be able to uncover and preserve the true data structure. Meanwhile, a reliable and easy-to-obtain supervision is desirable for practical learning. To meet these two objectives, in this paper we make the first attempt towards utilizing the orderly relationship, such as the data a is closer to b than to c, as a novel supervision. We propose an orderly subspace clustering approach with a novel regularization term. OSC enforces the learned representations to simultaneously capture the intrinsic subspace structure and reveal orderly structure that is faithful to true data relationship. Experimental results with several benchmarks have demonstrated that aside from more accurate clustering against state-of-the-arts, OSC interprets orderly data structure which is beyond what current approaches can offer.

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