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Found 4 papers, 4 papers with code
Object Tracking by Detection with Visual and Motion Cues
The assignment heuristic relies on four metrics: An embedding vector that describes the appearance of objects and can be used to re-identify them, a displacement vector that describes the object movement between two consecutive video frames, the Mahalanobis distance between the Kalman filter states and the new detections, and a class distance.
Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection
Our approach is motivated by logistics, where this assumption is valid and refined planes can be used to perform robust object detection without the need for supervised learning.
FeatureNMS: Non-Maximum Suppression by Learning Feature Embeddings
Most state of the art object detectors output multiple detections per object.
Simultaneous Object Detection and Semantic Segmentation
Both object detection in and semantic segmentation of camera images are important tasks for automated vehicles.
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