Neuromorphic vision sensors (NVS) with the features of small data redundancy and transmission latency are widely implemented in Internet of Things applications. Previous studies have developed various object detection algorithms based on NVS’s unique event data format. However, most of these methods are only adaptive for scenarios with stationary backgrounds. Under dynamic background conditions, NVS can also acquire the events of non-target objects due to its mechanism of detecting pixel intensity changes. As a result, the performance of existing detection methods is greatly degraded. To address this shortcoming, we introduce an extra refinement process to the conventional histogram-based (HIST) detection method. For the proposed regions from HIST, we apply a practical decision condition to categorize them as either object-dominant or background-dominant cases. Then, the object-dominant regions undergo a second-time HIST-based region proposal for precise localization, while background-dominant regions employ an upper outline determination strategy for target object identification. Finally, the refined results are tracked using a simplified Kalman filter approach. Evaluated in an outdoor drone surveillance with an event camera, the proposed scheme demonstrates superior performance in both intersection over union and F1 score metrics compared to other methods.