Video-to-Event Pipeline With Continuous Event Sampling
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Solution Overview
Problem
Existing methods for converting video frames to event data in neuromorphic cameras face challenges in accurately generating realistic and continuous event streams, particularly due to the dynamic range gap between standard APS and DVS cameras, and the lack of large-scale annotated datasets, which affects tasks like pose estimation and optical flow estimation.
Innovation Solution
A video-to-event prediction pipeline system that includes a backbone conversion network and an event sampling module, utilizing a 3D UNet to encode input frame sequences, a motion-aware event voxel prediction stage, and a hybrid loss structure to generate high-fidelity event streams by preserving temporal continuity and nonlinear dynamics, along with a pose estimation pipeline that uses a modified TORE volume and early-exit event filtering for accurate pose estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If standard APS cameras are used to capture video frames, then the system is cost-effective and suitable for common applications, but the frame rate (around 30 fps) is insufficient to capture high-speed non-linear motion
Solution Approach 1:
The patent creates synthetic event data by copying and transforming information from standard APS video frames. The event simulation module generates artificial event streams that mimic the output of expensive high-speed event cameras, allowing training of pose estimation models without requiring actual high-speed event camera hardware.
Solution Approach 2:
The patent introduces an intermediate representation layer that translates APS frame data into simulated event data. This intermediary transformation enables the system to bridge the gap between standard camera output and the event-based format required by neuromorphic pose estimation algorithms, avoiding the need for expensive high-speed event cameras.
2Speed
If very high frame rate cameras (1000, 10,000 fps) are used to capture high-speed motion, then the temporal resolution is sufficient, but the system becomes very expensive and unsuitable for common applications
Solution Approach 1:
The patent creates synthetic event data by copying and transforming information from standard APS video frames. The event simulation module generates artificial event streams that mimic the output of expensive high-speed event cameras, allowing training of pose estimation models without requiring actual high-speed event camera hardware.
Solution Approach 2:
The patent uses computationally inexpensive synthetic event data generated from standard video frames as a substitute for expensive high-speed event camera recordings. This synthetic data serves as a disposable, cost-effective alternative that provides sufficient training quality without requiring expensive hardware.
3Measurement precision
If event-based sensing is used to achieve high temporal resolution and high dynamic range, then the capture rate and performance in extreme lighting conditions improve, but the data is sparse and labeling is difficult due to the dynamic range gap between APS and DVS
Solution Approach 1:
The patent introduces an intermediate representation layer that translates APS frame data into simulated event data. This intermediary transformation enables the system to bridge the gap between standard camera output and the event-based format required by neuromorphic pose estimation algorithms, avoiding the need for expensive high-speed event cameras.
Solution Approach 2:
The patent performs preliminary generation of synthetic event data and creation of ground truth labels before the actual pose estimation task. By pre-generating labeled event streams from standard video sequences, the system eliminates the labeling bottleneck and makes the data ready for immediate use in training, avoiding the difficulty of manual annotation.
4Device complexity
If existing video-to-event conversion methods are used, then the conversion process is simple, but the generated event streams lack temporal continuity and realism due to the dynamic range gap between APS and DVS cameras
Solution Approach 1:
The patent implements a dynamic event simulation model that adapts to varying lighting conditions and motion patterns. The simulation module dynamically adjusts event generation parameters based on the input video content, preserving temporal continuity and realism while maintaining reasonable computational complexity.
Solution Approach 2:
The patent transforms the conversion approach by changing key parameters of the event generation process. Instead of simple threshold-based conversion, the system uses learned parameter transformations that account for the dynamic range gap between APS and DVS cameras, significantly improving the realism and temporal continuity of generated event streams.
Data Source
AI summary
A video to event prediction pipeline system includes a backbone conversion network having a model that is configured to receive a raw active pixel sensor video sequence and convert it into 3D predicted voxels. An event sampling module is configured to receive the 3D predicted voxels and create event timestamps in a continuous scale by leveraging nonlinear dynamics of event firing trends in each voxel of the 3D predicted voxels. The backbone conversion network comprises a series of training loss function modules, the training loss function modules teaching the backbone conversion network to account for variations in the active pixel sensor video sequence caused by adjustable camera parameters of the active pixel sensor video sequence.