Issues in Object Detection in Videos using Common Single-Image CNNs
This addresses the issue of frame-to-frame detection errors in applications like autonomous vehicles, though it is incremental as it builds on existing neural networks and datasets.
The paper tackles the problem of inconsistent object detection across video frames by proposing a method to generate training datasets with ground-truth layers for moving objects, using FlowNet2-Pytorch and segmentation masks to create a loss function for improving neural network consistency.
A growing branch of computer vision is object detection. Object detection is used in many applications such as industrial process, medical imaging analysis, and autonomous vehicles. The ability to detect objects in videos is crucial. Object detection systems are trained on large image datasets. For applications such as autonomous vehicles, it is crucial that the object detection system can identify objects through multiple frames in video. There are many problems with applying these systems to video. Shadows or changes in brightness that can cause the system to incorrectly identify objects frame to frame and cause an unintended system response. There are many neural networks that have been used for object detection and if there was a way of connecting objects between frames then these problems could be eliminated. For these neural networks to get better at identifying objects in video, they need to be re-trained. A dataset must be created with images that represent consecutive video frames and have matching ground-truth layers. A method is proposed that can generate these datasets. The ground-truth layer contains only moving objects. To generate this layer, FlowNet2-Pytorch was used to create the flow mask using the novel Magnitude Method. As well, a segmentation mask will be generated using networks such as Mask R-CNN or Refinenet. These segmentation masks will contain all objects detected in a frame. By comparing this segmentation mask to the flow mask ground-truth layer, a loss function is generated. This loss function can be used to train a neural network to be better at making consistent predictions on video. The system was tested on multiple video samples and a loss was generated for each frame, proving the Magnitude Method's ability to be used to train object detection neural networks in future work.