Tracking Objects as Pixel-wise Distributions

Multi-object tracking (MOT) requires detecting and associating objects through frames. Unlike tracking via detected bounding boxes or tracking objects as points, we propose tracking objects as pixel-wise distributions. We instantiate this idea on a transformer-based architecture, P3AFormer, with pixel-wise propagation, prediction, and association. P3AFormer propagates pixel-wise features guided by flow information to pass messages between frames. Furthermore, P3AFormer adopts a meta-architecture to produce multi-scale object feature maps. During inference, a pixel-wise association procedure is proposed to recover object connections through frames based on the pixel-wise prediction. P3AFormer yields 81.2\% in terms of MOTA on the MOT17 benchmark -- the first among all transformer networks to reach 80\% MOTA in literature. P3AFormer also outperforms state-of-the-arts on the MOT20 and KITTI benchmarks.Comment: Accepted in ECCV22 as an oral presentation paper. The code&project page is at https://github.com/dvlab-research/ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distribution

Iterative multi-path tracking for video and volume segmentation with sparse point supervision

Recent machine learning strategies for segmentation tasks have shown great ability when trained on large pixel-wise annotated image datasets. It remains a major challenge however to aggregate such datasets, as the time and monetary cost associated with collecting extensive annotations is extremely high. This is particularly the case for generating precise pixel-wise annotations in video and volumetric image data. To this end, this work presents a novel framework to produce pixel-wise segmentations using minimal supervision. Our method relies on 2D point supervision, whereby a single 2D location within an object of interest is provided on each image of the data. Our method then estimates the object appearance in a semi-supervised fashion by learning object-image-specific features and by using these in a semi-supervised learning framework. Our object model is then used in a graph-based optimization problem that takes into account all provided locations and the image data in order to infer the complete pixel-wise segmentation. In practice, we solve this optimally as a tracking problem using a K-shortest path approach. Both the object model and segmentation are then refined iteratively to further improve the final segmentation. We show that by collecting 2D locations using a gaze tracker, our approach can provide state-of-the-art segmentations on a range of objects and image modalities (video and 3D volumes), and that these can then be used to train supervised machine learning classifiers

Visual object tracking

University of Technology Sydney. Faculty of Engineering and Information Technology.Visual object tracking is a critical task in many computer-vision-related applications, such as surveillance and robotics. If the tracking target is provided in the first frame of a video, the tracker will predict the location and the shape of the target in the following frames. Despite the significant research effort that has been dedicated to this area for several years, this field remains challenging due to a number of issues, such as occlusion, shape variation and drifting, all of which adversely affect the performance of a tracking algorithm. This research focuses on incorporating the spatial and temporal context to tackle the challenging issues related to developing robust trackers. The spatial context is what surrounds a given object and the temporal context is what has been observed in the recent past at the same location. In particular, by considering the relationship between the target and its surroundings, the spatial context information helps the tracker to better distinguish the target from the background, especially when it suffers from scale change, shape variation, occlusion, and background clutter. Meanwhile, the temporal contextual cues are beneficial for building a stable appearance representation for the target, which enables the tracker to be robust against occlusion and drifting. In this regard, we attempt to develop effective methods that take advantage of the spatial and temporal context to improve the tracking algorithms. Our proposed methods can benefit three kinds of mainstream tracking frameworks, namely the template-based generative tracking framework, the pixel-wise tracking framework and the tracking-by-detection framework. For the template-based generative tracking framework, a novel template based tracker is proposed that enhances the existing appearance model of the target by introducing mask templates. In particular, mask templates store the temporal context represented by the frame difference in various time scales, and other templates encode the spatial context. Then, using pixel-wise analytic tools which provide richer details, which naturally accommodates tracking tasks, a finer and more accurate tracker is proposed. It makes use of two convolutional neural networks to capture both the spatial and temporal context. Lastly, for a visual tracker with a tracking-by-detection strategy, we propose an effective and efficient module that can improve the quality of the candidate windows sampled to identify the target. By utilizing the context around the object, our proposed module is able to refine the location and dimension of each candidate window, thus helping the tracker better focus on the target object