An official website of the United States government
Abstract Object tracking in microscopy videos is crucial for understanding biological processes. While existing methods often require fine-tuning tracking algorithms to fit the image dataset, here we explored an alternative paradigm: augmenting the image time-lapse dataset to fit the tracking algorithm. To test this approach, we evaluated whether generative video frame interpolation can augment the temporal resolution of time-lapse microscopy and facilitate object tracking in multiple biological contexts. We systematically compared the capacity of Latent Diffusion Model for Video Frame Interpolation (LDMVFI), Real-time Intermediate Flow Estimation (RIFE), Compression-Driven Frame Interpolation (CDFI), and Frame Interpolation for Large Motion (FILM) to generate synthetic microscopy images derived from interpolating real images. Our testing image time series ranged from fluorescently labeled nuclei to bacteria, yeast, cancer cells, and organoids. We showed that the off-the-shelf frame interpolation algorithms produced bio-realistic image interpolation even without dataset-specific retraining, as judged by high structural image similarity and the capacity to produce segmentations that closely resemble results from real images. Using a simple tracking algorithm based on mask overlap, we confirmed that frame interpolation significantly improved tracking across several datasets without requiring extensive parameter tuning and capturing complex trajectories that were difficult to resolve in the original image time series. Taken together, our findings highlight the potential of generative frame interpolation to improve tracking in time-lapse microscopy across diverse scenarios, suggesting that a generalist tracking algorithm for microscopy could be developed by combining deep learning segmentation models with generative frame interpolation.
more »
« less
Incorporating Event Information for High-Quality Video Interpolation
Optical flow-based video interpolation is a commonly used method to enrich video details by generating new intermediate frames from existing ones. However, it cannot accurately reproduce the trajectories of irregular and fast-moving objects. To achieve the accurate reconstruction of high-speed scenes, incorporating event information during the interpolation process is an effective method. However, existing methods are not suitable for scenarios where events are sparse. To implement high-quality video interpolation for these scenarios, this study incorporates event information into the interpolation process with three-fold ideas: a) treating the moving target as the foreground and using events to delineate the target area, b) matching foreground to background based on the event information to generate temporal frame between keyframes, and c) generating intermediate frames between keyframe and temporal frame with optical flow interpolation. Empirical studies indicate that owing to its efficient incorporation of event information, the proposed framework outperforms state-of-the-art methods in generating high-quality frames for video interpolation.
more »
« less
- Award ID(s):
- 2153440
- PAR ID:
- 10497853
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-6950-2
- Page Range / eLocation ID:
- 1 to 6
- Subject(s) / Keyword(s):
- Video Interpolation optical flow event camera
- Format(s):
- Medium: X
- Location:
- Marseille, France
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
In this paper, we present a new inpainting framework for recovering missing regions of video frames. Compared with image inpainting, performing this task on video presents new challenges such as how to preserving temporal consistency and spatial details, as well as how to handle arbitrary input video size and length fast and efficiently. Towards this end, we propose a novel deep learning architecture which incorporates ConvLSTM and optical flow for modeling the spatial-temporal consistency in videos. It also saves much computational resource such that our method can handle videos with larger frame size and arbitrary length streamingly in real-time. Furthermore, to generate an accurate optical flow from corrupted frames, we propose a robust flow generation module, where two sources of flows are fed and a flow blending network is trained to fuse them. We conduct extensive experiments to evaluate our method in various scenarios and different datasets, both qualitatively and quantitatively. The experimental results demonstrate the superior of our method compared with the state-of-the-art inpainting approaches.more » « less
-
Online action detection is the task of predicting the action as soon as it happens in a streaming video. A major challenge is that the model does not have access to the future and has to solely rely on the history, i.e., the frames observed so far, to make predictions. It is therefore important to accentuate parts of the history that are more informative to the prediction of the current frame. We present GateHUB, Gated History Unit with Background Suppression, that comprises a novel position-guided gated cross-attention mechanism to enhance or suppress parts of the history as per how informative they are for current frame prediction. GateHUB further proposes Future-augmented History (FaH) to make history features more informative by using subsequently observed frames when available. In a single unified framework, GateHUB integrates the transformer's ability of long-range temporal modeling and the recurrent model's capacity to selectively encode relevant information. GateHUB also introduces a background suppression objective to further mitigate false positive background frames that closely resemble the action frames. Extensive validation on three benchmark datasets, THUMOS, TVSeries, and HDD, demonstrates that GateHUB significantly outperforms all existing methods and is also more efficient than the existing best work. Furthermore, a flow-free version of GateHUB is able to achieve higher or close accuracy at 2.8x higher frame rate compared to all existing methods that require both RGB and optical flow information for prediction.more » « less
-
null (Ed.)The emerging satellite videos provide the opportunity to detect moving objects and track their trajectories, which were not possible for remotely sensed imagery with limited temporal resolution. So far, most studies using satellite video data have been concentrated on traffic monitoring through detecting and tracking moving cars, whereas the studies on other moving objects such as airplanes are limited. In this paper, an integrated method for monitoring moving airplanes from a satellite video is proposed. First, we design a normalized frame difference labeling (NFDL) algorithm to detect moving airplanes, which adopts a non-recursive strategy to deliver stable detection throughout the whole video. Second, the template matching (TM) technique is utilized for tracking the detected moving airplanes in the frame sequence by improved similarity measures (ISMs) with better rotation invariance and model drift suppression ability. Template matching with improved similarity measures (TM-ISMs) is further implemented to handle the leave-the-scene problem. The developed method is tested on a satellite video to detect and track eleven moving airplanes. Our NFDL algorithm successfully detects all the moving airplanes with the highest F1 score of 0.88 among existing algorithms. The performance of TM-ISMs is compared with both its traditional counterparts and other state-of-the-art tracking algorithms. The experimental results show that TM-ISMs can handle both rotation and leave-the-scene problems. Moreover, TM-ISMs achieve a very high tracking accuracy of 0.921 and the highest tracking speed of 470.62 frames per second.more » « less
-
Event-based cameras have shown great promise in a variety of situations where frame based cameras suffer, such as high speed motions and high dynamic range scenes. However, developing algorithms for event measurements requires a new class of hand crafted algorithms. Deep learning has shown great success in providing model free solutions to many problems in the vision community, but existing networks have been developed with frame based images in mind, and there does not exist the wealth of labeled data for events as there does for images for supervised training. To these points, we present EV-FlowNet, a novel self-supervised deep learning pipeline for optical flow estimation for event based cameras. In particular, we introduce an image based representation of a given event stream, which is fed into a self-supervised neural network as the sole input. The corresponding grayscale images captured from the same camera at the same time as the events are then used as a supervisory signal to provide a loss function at training time, given the estimated flow from the network. We show that the resulting network is able to accurately predict optical flow from events only in a variety of different scenes, with performance competitive to image based networks. This method not only allows for accurate estimation of dense optical flow, but also provides a framework for the transfer of other self-supervised methods to the event-based domain.more » « less
