Temporal Deformable Kernels for Accurate Video Object Tracking
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Solution Overview
Problem
Existing machine-learning models for object identification and tracking in image data, such as those used in autonomous vehicles, suffer from inaccuracies and inefficiencies due to their inability to effectively utilize temporal information and deformable kernels, leading to flawed object detection and segmentation.
Innovation Solution
Implementing temporal-based deformable convolutions in neural networks that learn and apply offset fields to regular kernels based on image content from past and current images, allowing for the generation of deformed kernels for improved object detection, classification, and segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine-learning models are used for object detection, then the system is simple to implement, but the detection accuracy and precision are flawed
Solution Approach 1:
The patent applies deformable convolutions that dynamically adjust kernel shapes and positions based on learned offset fields, allowing the model to adapt to object deformations and movements in video sequences. This dynamic adaptation improves detection accuracy by matching the actual object geometry rather than using fixed kernels
Solution Approach 2:
The patent introduces temporal dimension by incorporating video sequences and using temporal convolutions to process information across multiple time frames. This additional temporal dimension enables the model to track object motion and improve detection precision through temporal context
2Reliability
If temporal information is not utilized, then the computational process is simple, but object tracking and trajectory planning are inaccurate
Solution Approach 1:
The patent performs preliminary actions by pre-processing video frames and extracting temporal features before main detection. The model learns offset fields and deformation patterns from temporal sequences in advance, preparing the data for more accurate object tracking and reducing complexity during real-time detection
Solution Approach 2:
The patent introduces intermediate representations such as offset fields and deformation vectors that mediate between raw temporal video data and final detection results. These intermediaries capture temporal motion patterns without requiring complex direct processing of entire video sequences
3Adaptability or versatility
If regular kernels are used for convolution, then the computational process is efficient, but the ability to handle object deformation and motion is limited
Solution Approach 1:
The patent applies local quality by using deformable convolutions that adjust kernel properties locally based on learned offset fields. Each kernel can have different shapes and positions adapted to local object characteristics, improving deformation handling while maintaining computational efficiency through selective adaptation rather than global complexity
4Measurement precision
If accurate object segmentation is achieved through complex models, then detection precision improves, but computational time increases
Solution Approach 1:
The patent applies partial action by focusing computational resources on relevant regions. Deformable convolutions with learned offset fields concentrate processing on areas with object deformations or motions, rather than uniformly processing entire images. This partial focus achieves accurate segmentation while reducing overall computational time
Data Source
AI summary
Techniques are disclosed for implementing a convolutional neural network that determines an offset field for deforming a kernel to be used in a convolution. The offset field is temporally-based, at least in part, on data generated at an earlier time. Furthermore, techniques are disclosed for using sensor data to train a neural network to learn shapes or configurations of such deformed kernels. The temporal-based deformable convolutions may be used for object identification, object matching, object classification, segmentation, and/or object tracking, in various examples.