Abstract: This document describes methods and systems directed at imaging sensor and radar fusion for multiple-object tracking. Using tracking-by-detection, an object is first detected in a frame captured by an imaging sensor, and then the object is tracked over several consecutive frames by both the imaging sensor and a radar system. The object is tracked by assigning a probability that the object identified in one frame is a same object identified in the consecutive frame. A probability is calculated for each data set captured by a sensor by a supervised-learning neural-network model using the data collected from the sensors. Then, the probabilities associated with each sensor are fused into a refined probability. By fusing the data gathered by the imaging sensor and the radar system in the consecutive frames, a safety system can track multiple objects more accurately and reliably than using the sensor data separately to track objects.

Abstract: This document describes techniques and systems related to a radar system using a machine-learned model for stationary object detection. The radar system includes a processor that can receive radar data as time-series frames associated with electromagnetic (EM) energy. The processor uses the radar data to generate a range-time map of the EM energy that is input to a machine-learned model. The machine-learned model can receive as inputs extracted features corresponding to the stationary objects from the range-time map for multiple range bins at each of the time-series frames. In this way, the described radar system and techniques can accurately detect stationary objects of various sizes and extract critical features corresponding to the stationary objects.

Abstract: This document describes methods and systems directed at imaging sensor and radar fusion for multiple-object tracking. Using tracking-by-detection, an object is first detected in a frame captured by an imaging sensor, and then the object is tracked over several consecutive frames by both the imaging sensor and a radar system. The object is tracked by assigning a probability that the object identified in one frame is a same object identified in the consecutive frame. A probability is calculated for each data set captured by a sensor by a supervised-learning neural-network model using the data collected from the sensors. Then, the probabilities associated with each sensor are fused into a refined probability. By fusing the data gathered by the imaging sensor and the radar system in the consecutive frames, a safety system can track multiple objects more accurately and reliably than using the sensor data separately to track objects.