研究者詳細 - 黄　文浩

黄　文浩（コウ　ブンコウ）

Huang, Wenhao

写真a

所属（所属キャンパス）: 政策・メディア研究科（湘南藤沢）

職名: 特任助教（有期）

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Object Size Classification in Garbage Disposal Sensing System Using Monocular Depth Estimation

Ito T., Huang W., Chen Y., Nakazawa J.

Communications in Computer and Information Science 2650 CCIS 190 - 208 2026年

ISSN 18650929

　概要を見る

Deep learning-based object detection is widely used in urban sensing, enabling tasks such as pedestrian, pothole, and waste detection. Automotive sensing with dashcams facilitates large-scale, real-time detection across urban environments. However, existing studies primarily focus on detection without estimating object size, which is crucial for event classification. Conventional size estimation methods rely on RGB-D cameras, multiple cameras, or LIDAR, making them unsuitable for large-scale automotive sensing with single RGB dashcams. Monocular depth estimation provides relative depth but does not yield absolute size measurements. To address this limitation, we propose a novel approach that combines monocular depth estimation with a reference object of known size. By comparing the detected object’s pixel dimensions with those of the reference object, its physical size can be estimated. To validate our approach, we developed an automotive sensing platform that detected and quantified household garbage bags using footage from the rear-view camera of garbage trucks. The truck body serves as the reference object, ensuring reliable size estimation. Experiments conducted with real-world data collected using an NVIDIA Jetson TX2 demonstrated the effectiveness of our method. The proposed approach achieves size estimation accuracy with mean squared errors (MSEs) of 20.02 for width and 18.68 for height while maintaining an end-to-end processing rate of 19.21 frames per second (FPS) for detection, tracking, and size estimation.
- Access to Document (DOI)
QPilot: Reinforcement Learning-Based Adaptive Scheduling for Object-Sparse MOT

Lo C., Huang W., Tsuge A., Chen Y., Okoshi T., Nakazawa J.

IEEE International Conference on Pervasive Computing and Communications Workshops Percom Workshops （ 2026 ） 580 - 583 2026年

ISSN 28365348

　概要を見る

Edge computing plays a key role in smart-city deployments. Deploying AI models on edge devices reduces latency and privacy concerns. This work proposes QPilot, a reinforcement-learning-based adaptive model-switching method that dynamically selects detection models of different sizes for multi-object tracking (MOT) inference to balance efficiency and accuracy according to device compute capacity and scene complexity. In object-sparse scenarios, it improves tracking accuracy with only a small additional computational cost and achieves an optimal efficiency-accuracy trade-off in MOT. We validated the method on multiple hardware platforms, showing strong cross-device adaptability as well as feasibility and promise for edge environments.
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SORA-SORT: A Simple Occlusion Risk-Aware Framework for Multi-Object Tracking

Kato K., Goto D., Huang W., Tsuge A., Okoshi T., Nakazawa J.

Ubicomp Companion 2025 Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing 251 - 255 2025年12月

　概要を見る

To cost-effectively acquire people flow data, the use of bird's-eye view cameras that capture wide areas from a distance is being considered. However, videos captured by such cameras frequently exhibit dynamic occlusions, such as those caused by interactions among individuals, and static occlusions, such as those caused by fixed obstacles, leading to a decline in tracking performance. Therefore, this study develops a system to estimate dynamic and static occlusion risk using only detection and tracking information, proposing a general multi-object tracking (MOT) performance improvement method by dynamically controlling tracker parameters based on this risk. Comparative experiments against a baseline method demonstrated that our approach outperformed the baseline on most metrics, suggesting that our method contributes to enhancing tracking performance in occlusion-prone areas. However, an increase in ID switches (IDS) and a decrease in processing speed occurred. The increase in IDS is considered to be due to the unnecessary retention of tracks that should have been deleted due to lifespan extension. This result implies a trade-off between tracking continuity and the consistency of ID assignment.
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JumpQ: Stochastic Scheduling to Accelerating Object-detection-driven Mobile Sensing on Object-sparse Video Data

Mikami K., Huang W., Chen Y., Nakazawa J.

ACM Sensys 2025 23rd ACM Conference on Embedded Networked Sensor Systems in Transactions to Conference Embedded Artificial Intelligence and Sensing Systems 332 - 344 2025年05月

　概要を見る

Deep learning-based object detection has seen a surge in applications for sensing systems on mobile devices. In this context, objects are identified and tracked across video frames, facilitating the calculation of associated events of interest. A significant research challenge refers to the acceleration of processing speed, which is constrained by deep learning-based object detection due to its intensive resource requirements. This paper focuses on a typical mobile sensing scenario, wherein sequences of frames containing objects of interest are sparsely dispersed throughout the video stream. Given that many of the frames lack objects, allocating substantial computational resources to detect them becomes inefficient. In light of this, we propose a stochastic scheduling algorithm, JumpQ. JumpQ performs per-frame detection when anticipating the presence of objects in the current frames. Consecutive negative detections prompt a transition to intermittent detection with a probability that undergoes further decay if the negative detection persists until reaching a predefined limit. Upon a positive detection, JumpQ swiftly reverts to per-frame detection and retraces a specific number of previously buffered frames to ensure the inclusion of potentially missed true frames. A comprehensive experimental study using the garbage bag counting technique was conducted to show the efficiency of JumpQ in accelerating the processing speed by nearly 1.92 times while maintaining a negligible impact on sensing accuracy.
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AdaLine: Adaptive Counting Line Optimization by Perspective-Aware Trajectory Modeling in Object-Detection-Tracking Systems

Huang W., Chen Y., Nakazawa J., Okoshi T.

IEEE Access 13 111282 - 111292 2025年

　概要を見る

The combination of object detection, tracking, and counting has become a widely used method. The position and angle of cameras can vary according to the deployment scenarios, which affects counting accuracy. Traditional approaches often rely on manually pre-defined counting lines or regions-of-interest (ROIs), which are static, environment-specific, and difficult to generalize. To overcome these limitations, we propose AdaLine, an adaptive, perspective-aware algorithm that learns the optimal counting line from object trajectories, thereby enabling adaptation to diverse environments and camera viewpoints without manually defined counting lines. AdaLine adapts automatically as the scene evolves by clustering incoming trajectories with K-means, selecting the most stable line candidate, and smoothing it with an exponential moving average. Experimental evaluations across different scenarios and camera settings show that AdaLine achieves better performance in terms of accuracy, stability, and applicability. Our approach offers a scalable, real-time configuration-free solution for object-detection-tracking systems.
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