Abstract: An example apparatus for detecting objects in video frames includes a receiver to receive a plurality of video frames from a video camera. The apparatus also includes a first still image object detector to receive a first frame of the plurality of video frames and calculate localization information and confidence information for each potential object patch in the first frame. The apparatus further includes a second still image object detector to receive an adjacent frame of the plurality of video frames adjacent to the first frame and calculate localization information and confidence information for each potential object patch in the adjacent frame. The apparatus includes a similarity detector trained to detect paired patches between the first frame and the adjacent frame based on a comparison of the detected potential object patches.

Abstract: Techniques related to training and implementing a bidirectional pairing architecture for object detection are discussed. Such techniques include generating a first enhanced feature map for each frame of a video sequence by processing the frames in a first direction, generating a second enhanced feature map for frame by processing the frames in a second direction opposite the first, and determining object detection information for each frame using the first and second enhanced feature map for the frame.

Abstract: Techniques related to training and implementing a bidirectional pairing architecture for object detection are discussed. Such techniques include generating a first enhanced feature map for each frame of a video sequence by processing the frames in a first direction, generating a second enhanced feature map for frame by processing the frames in a second direction opposite the first, and determining object detection information for each frame using the first and second enhanced feature map for the frame.

Abstract: Systems, apparatuses and methods (30) may provide for technology that stores data associated with a plurality of intermediate operations in an autonomous vehicle process (32), generates a visualization output based at least partly on the data (34), and changes a magnification level of the visualization output based on user input (38), the visualization output is generated further based on parameter input and the data includes vector chart data.

Abstract: Techniques related to training and implementing a bidirectional pairing architecture for object detection are discussed. Such techniques include generating a first enhanced feature map for each frame of a video sequence by processing the frames in a first direction, generating a second enhanced feature map for frame by processing the frames in a second direction opposite the first, and determining object detection information for each frame using the first and second enhanced feature map for the frame.

Abstract: An example apparatus for detecting objects in video frames includes a receiver to receive a plurality of video frames from a video camera. The apparatus also includes a first still image object detector to receive a first frame of the plurality of video frames and calculate localization information and confidence information for each potential object patch in the first frame. The apparatus further includes a second still image object detector to receive an adjacent frame of the plurality of video frames adjacent to the first frame and calculate localization information and confidence information for each potential object patch in the adjacent frame. The apparatus includes a similarity detector trained to detect paired patches between the first frame and the adjacent frame based on a comparison of the detected potential object patches.

Abstract: Techniques related to training and implementing a bidirectional pairing architecture for object detection are discussed. Such techniques include generating a first enhanced feature map for each frame of a video sequence by processing the frames in a first direction, generating a second enhanced feature map for frame by processing the frames in a second direction opposite the first, and determining object detection information for each frame using the first and second enhanced feature map for the frame.