As one of the fundamental technologies for scene understanding, semantic segmentation has been widely explored in the last few years. Light field cameras encode the geometric information by simultaneously recording the spatial information and angular information of light rays, which provides us with a new way to solve this issue. In this paper, we propose a high-quality and challenging urban scene dataset, containing 1074 samples composed of real-world and synthetic light field images as well as pixel-wise annotations for 14 semantic classes. To the best of our knowledge, it is the largest and the most diverse light field dataset for semantic segmentation. We further design two new semantic segmentation baselines tailored for light field and compare them with state-of-the-art RGB, video and RGB-D-based methods using the proposed dataset. The outperforming results of our baselines demonstrate the advantages of the geometric information in light field for this task. We also provide evaluations of super-resolution and depth estimation methods, showing that the proposed dataset presents new challenges and supports detailed comparisons among different methods. We expect this work inspires new research direction and stimulates scientific progress in related fields. The complete dataset is available at https://github.com/HAWKEYE-Group/UrbanLF.

Global context information is particularly important for comprehensive scene understanding. It helps clarify local confusions and smooth predictions to achieve fine-grained and coherent results. However, most existing light field processing methods leverage convolution layers to model spatial and angular information. The limited receptive field restricts them to learn long-range dependency in LF structure. In this paper, we propose a novel network based on deep efficient transformers ( i.e., LF-DET) for LF spatial super-resolution. It develops a spatial-angular separable transformer encoder with two modeling strategies termed as sub-sampling spatial modeling and multi-scale angular modeling for global context interaction. Specifically, the former utilizes a sub-sampling convolution layer to alleviate the problem of huge computational cost when capturing spatial information within each sub-aperture image. In this way, our model can cascade more transformers to continuously enhance feature representation with limited resources. The latter processes multi-scale macro-pixel regions to extract and aggregate angular features focusing on different disparity ranges to well adapt to disparity variations. Besides, we capture strong similarities among surrounding pixels by dynamic positional encodings to fill the gap of transformers that lack of local information interaction. The experimental results on both real-world and synthetic LF datasets confirm our LF-DET achieves a significant performance improvement compared with state-of-the-art methods. Furthermore, our LF-DET shows high robustness to disparity variations through the proposed multi-scale angular modeling.

Recently, tracking-by-detection has become a popular paradigm in Multiple-object tracking (MOT) for its concise pipeline. Many current works first associate the detections to form track proposals and then score proposalns by manual functions to select the best. However, long-term tracking information is lost in this way due to detection failure or heavy occlusion. In this paper, the Extendable Multiple Nodes Tracking framework (EMNT) is introduced to model the association. Instead of detections, EMNT creates four basic types of nodes including correct, false, dummy and termination to generally model the tracking procedure. Further, we propose a General Recurrent Tracking Unit (RTU++) to score track proposals by capturing long-term information. In addition, we present an efficient generation method of simulated tracking data to overcome the dilemma of limited available data in MOT. The experiments show that our methods achieve state-of-the-art performance on MOT17, MOT20 and HiEve benchmarks. Meanwhile, RTU++ can be flexibly plugged into other trackers such as MHT, and bring significant improvements. The additional experiments on MOTS20 and CTMC-v1 also demonstrate the generalization ability of RTU++ trained by simulated data in various scenarios.

The rapid increase in the volume of video data generated from edges in the Industrial Internet of Things, opens up new possibilities for enhancing the application of video service. Multicamera multiobject tracking (MCMT) has always been a fundamental task in video surveillance or traffic control. However, the traditional MCMT methods are limited by the communication bottleneck and computation resources of the centralized curator, and suffer from security and privacy issues. In this article, we first design multicamera multihypothesis tracking (MC-MHT) framework to achieve real-time tracking performance among edge cameras. The complex association of objects is described by multiskip trees. The tracking task is well distributed to each camera. Then, we integrate multicamera tracking chain into MC-MHT to ensure security and trust. The state transition of targets in multicamera is illustrated from the perspective of blockchain transactions. The transactions are validated by an integrated tracking consensus to counter Byzantine behavior. Numerical results derived from real-world scenarios and CAMPUS dataset show that the proposed method achieves real-time performance (24–36 FPs) and 79.0–82.4 MOTA indicator, as well as reduces identity switch errors about 71% under Byzantine attack.

Light field (LF) depth estimation is a crucial basis for LF-related applications. Most existing methods are based on the Lambertian assumption and cannot deal with non-Lambertian surfaces represented by transparent objects and mirrors. In this paper, we propose a novel Adaptive-Cross-Operator-based(ACO) depth estimation algorithm for non-Lambertian LF. By analyzing the imaging characteristics of non-Lambertian regions, it is found that the difficulty of depth estimation lies in the photo inconsistency of the center view. Combining with the two-branch structure, we propose ACO with an inter-branch cooperation strategy to adaptively separate depth information with different reflectance coefficients. We discover that the bimodal distribution feature of the operator filtering results can assist in the separation of multi-layer scene information. The first detection branch filters the EPI and implicitly records the severity of multi-layer scene aliasing. According to the identification of bimodal distribution features, the non-Lambertian regions are marked out and the depth of the foreground is estimated. The second branch receives guidance from the first to dynamically adjust the inner weight and infer the background’s depth after weakening the interference from the foreground. Finally, the depth information separation of multi-layer scenes is achieved by extracting the unique X-shaped linear structure. Without the reflection coefficients of the non-Lambertian object, the proposed method can produce high-quality depth estimation under the transparency of 90% to 20%. Experimental results show that the proposed ACO outperforms state-of-the-art LF depth estimation methods in terms of accuracy and robustness.

Compared with external-supervised learning-based (ESLB) methods, self-supervised learning-based (SSLB) methods can overcome the domain gap problem caused by different light field (LF) acquisition conditions, which results in the performance degradation of light field super-resolution on unseen test datasets. Current SSLB methods exploit the cross-scale recurrence feature in the single view image for super-resolution, ignoring the correlation information among views. Different from previous works, we propose a cross-view recurrence-based self-supervised mapping framework to correlate complementary information among views in the down-scaled input LF. Specifically, the cross-view recurrence information consists of geometry structure features and similar structure features. The former is to provide sub-pixel information according to disparity correlations among adjacent views, and the latter is to acquire similar color and contour information among arbitrary views, which can compensate for error disparity guidance of geometry structure features in sharp variance areas. Moreover, instead of the widely used “All-to-All” strategy, we propose a “Part-to-Part” mapping strategy, which is better competent for SSLB approaches with limited training examples solely extracted from input LF. Finally, considering that self-supervised methods need to retrain from the beginning toward each test image, based on the proposed “part-to-part” strategy, an efficient end-to-end network is designed to extract these cross-view features for superior SASR performance with less training time. Experiment results demonstrate that our method outperforms other state-of-the-art ESLB methods on both large and small domain gap cases. Compared with the only SSLB method (LFZSSR [1]), our approach achieves better performance with 524 times less training time.