Animal behavior recognition is crucial for animal science, smart agriculture, and wildlife monitoring. However, existing keypoint- and detection-based methods rely on implicit feature learning and lack structured reasoning, while recent vision–language models remain suboptimal for animal behavior understanding due to limited domain knowledge and high intra-class variability. To address these challenges, we present the first systematic exploration of multi-step Chain-of-Thought reasoning for animal behavior recognition, proposing Animal-CoT, a framework that decomposes behavior understanding into interpretable steps, including pose perception, contextual reasoning, behavior hypothesis generation, and inverse verification. This iterative reasoning process reduces semantic ambiguity and enhances robustness in challenging visual conditions. We further extend existing datasets with explicit multi-step reasoning annotations, resulting, to the best of our knowledge, in the first animal behavior recognition datasets that enable structured multimodal training. Extensive experiments on multiple benchmarks show that Animal-CoT consistently outperforms traditional methods and state-of-the-art multimodal VLMs, achieving up to 49.7% F1-score improvement on CBVD-5 test set, which demonstrates the effectiveness of structured Chain-of-Thought reasoning for animal behavior recognition. Source code and data are available at https://github.com/WesLee88524/Animal-CoT .

Real-time tracking of subway riding will provide great convenience to millions of commuters in metropolitan areas. Traditional approaches using timetables need continuous attentions from the subway riders and are limited to the poor accuracy of estimating the travel time. Recent approaches using mobile devices rely on GSM and WiFi, which are not always available underground. In this work, we present SubTrack, utilizing sensors on mobile devices to provide automatic tracking of subway riding in real time. The real-time automatic tracking covers three major aspects of a passenger: detection of entering a station, tracking the passenger's position, and estimating the arrival time of subway stops. In particular, SubTrack employs the cell ID to first detect a passenger entering a station and exploits inertial sensors on the passenger's mobile device to track the train ride. Our algorithm takes the advantages of the unique vibrations in acceleration and typical moving patterns of the train to estimate the train's velocity and the corresponding position, and further predict the arrival time in real time. Our extensive experiments in two cities in China and USA respectively demonstrate that our system can accurately track the position of subway riders, predict the arrival time and push the arrival notification in a timely manner.

In this Demo, we present the design and implementation of a novel human localization and tracking system based on pyroelectric infrared(PIR) sensors. It is a new approach to setup several low-cost sensors to improve the simplicity of deployment. We first present a novel design in mechanical architecture, circuit board and sensors which enables our PIR sensor based system to detect the moving objects and track human beings in real time. Different from the previous work which only adopts binary output, we further explore the usage of analog signals from the sensors. This improvement is beneficial to accurate estimation of the distance between the device and human body. We have implemented several prototypes for evaluation and demonstration. According to the experimental results, our system can achieve an accuracy of 0.113m in an area of 12m × 6m.

With the proliferation of context-aware services and human-computer interaction scenarios, the novel techniques of motion detection have attracted growing interests. There are many products which use camera, infrared sensors, RF, etc. With more Wi-Fi devices in homes and the ability to measure the PHY layer information, Wi-Fi based motion detection has attracted more attention than before. In this poster, we present the design of RoMD, a robust motion detection system which uses existing Wi-Fi devices. Changing environment and human movement would cause significant pattern changes of the CSI amplitude over time. The basic idea of RoMD is to eliminate the “bad antennas” and interference of CSI measurements so that the features we extract would not be affected by changing environments. To achieve this goal, we implement an efficient data processing structure to accurately extract the feature and identify the human motion. Then, we simply evaluate the scheme and demonstrate feasibility. And finally, we introduce the work in the future.