Fine-Grained Walking Activity Recognition via Driving Recorder Dataset

The paper presents a fine-grained walking activity recognition toward an inferring pedestrian intention which is an important topic to predict and avoid a pedestrian's dangerous activity. The fine-grained activity recognition is to distinguish different activities between subtle changes such as walking with different directions. We believe a change of pedestrian's activity is significant to grab a pedestrian intention. However, the task is challenging since a couple of reasons, namely (i) in-vehicle mounted camera is always moving (ii) a pedestrian area is too small to capture a motion and shape features (iii) change of pedestrian activity (e.g. walking straight into turning) has only small feature difference. To tackle these problems, we apply vision-based approach in order to classify pedestrian activities. The dense trajectories (DT) method is employed for high-level recognition to capture a detailed difference. Moreover, we additionally extract detection-based region-of-interest (ROI) for higher performance in fine-grained activity recognition. Here, we evaluated our proposed approach on "self-collected dataset" and "near-miss driving recorder (DR) dataset" by dividing several activities-crossing, walking straight, turning, standing and riding a bicycle. Our proposal achieved 93.7% on the self-collected NTSEL traffic dataset and 77.9% on the near-miss DR dataset.