The paper presents a pedestrian near-miss detector with temporal analysis that provides both pedestrian detection and risk-level predictions which are demonstrated on a self-collected database. Our work makes three primary contributions: (i) The framework of pedestrian near-miss detection is proposed by providing both a pedestrian detection and risk-level assignment. Specifically, we have created a Pedestrian Near-Miss (PNM) dataset that categorizes traffic near-miss incidents based on their risk levels (high-, low-, and no-risk). Unlike existing databases, our dataset also includes manually localized pedestrian labels as well as a large number of incident-related videos. (ii) Single-Shot MultiBox Detector with Motion Representation (SSD-MR) is implemented to effectively extract motion-based features in a detected pedestrian. (iii) Using the self-collected PNM dataset and SSD-MR, our proposed method achieved +19.38% (on risk-level prediction) and +13.00% (on joint pedestrian detection and risk-level prediction) higher scores than that of the baseline SSD and LSTM. Additionally, the running time of our system is over 50 fps on a graphics processing unit (GPU).