In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

The AI City Challenge was created with two goals in mind: (1) pushing the boundaries of research and development in intelligent video analysis for smarter cities use cases, and (2) assessing tasks where the level of performance is enough to cause real-world adoption. Transportation is a segment ripe for such adoption. The fifth AI City Challenge attracted 305 participating teams across 38 countries, who leveraged city-scale real traffic data and high-quality synthetic data to compete in five challenge tracks. Track 1 addressed video-based automatic vehicle counting, where the evaluation being conducted on both algorithmic effectiveness and computational efficiency. Track 2 addressed city-scale vehicle re-identification with augmented synthetic data to substantially increase the training set for the task. Track 3 addressed city-scale multi-target multi-camera vehicle tracking. Track 4 addressed traffic anomaly detection. Track 5 was a new track addressing vehicle retrieval using natural language descriptions. The evaluation system shows a general leader board of all submitted results, and a public leader board of results limited to the contest participation rules, where teams are not allowed to use external data in their work. The public leader board shows results more close to real-world situations where annotated data is limited. Results show the promise of AI in Smarter Transportation. State-of-the-art performance for some tasks shows that these technologies are ready for adoption in real-world systems.

/pdf/the-5th-ai-city-challenge-1ch9d13xuy.pdf

The 5th AI City Challenge

The AI City Challenge was created to accelerate intelligent video analysis that helps make cities smarter and safer. Transportation is one of the largest segments that can benefit from actionable insights derived from data captured by sensors, where computer vision and deep learning have shown promise in achieving large-scale practical deployment. The 4th annual edition of the AI City Challenge has attracted 315 participating teams across 37 countries, who leverage city-scale real traffic data and high-quality synthetic data to compete in four challenge tracks. Track 1 addressed video-based automatic vehicle counting, where the evaluation is conducted on both algorithmic effectiveness and computational efficiency. Track 2 addressed city-scale vehicle re-identification with augmented synthetic data to substantially increase the training set for the task. Track 3 addressed city-scale multi-target multi-camera vehicle tracking. Track 4 addressed traffic anomaly detection. The evaluation system shows two leader boards, in which a general leader board shows all submitted results, and a public leader board shows results limited to our contest participation rules, that teams are not allowed to use external data in their work. The general leader board shows results more close to real-world situations where annotated data are limited. Our results show promise that AI technology can enable smarter and safer transportation systems.

/pdf/the-4th-ai-city-challenge-5f5gaxo728.pdf

The 4th AI City Challenge

Multi-camera tracking of vehicles on a city-scale level is a crucial task for efficient traffic monitoring. Most of the errors made by such multi-target multi-camera tracking systems arise due to tracking failures or misleading visual information of detection boxes under occlusion. Therefore, we propose an occlusion-aware approach that leverages temporal information from tracks to improve the single-camera tracking performance by an occlusion handling strategy and additional modules to filter false detections. For the multi-camera tracking, we discard obstacle-occluded detection boxes by a background filtering technique and boxes overlapping with other targets using the available track information to improve the quality of extracted visual features. Furthermore, topological and temporal constraints are incorporated to simplify the re-identification task in the multi-camera clustering. We give detailed insights into our method with ablative experiments and show its competitiveness on the CityFlowV2 dataset, where we achieve promising results ranking 4th in Track 3 of the 2021 AI City Challenge.

/pdf/an-occlusion-aware-multi-target-multi-camera-tracking-system-3mmmpn6ty5.pdf

An Occlusion-aware Multi-target Multi-camera Tracking System

Multi-person tracking is often solved with a tracking-by-detection approach that matches all tracks and detections simultaneously based on a distance matrix. In crowded scenes, ambiguous situations with similar track-detection distances occur, which leads to wrong assignments. To mitigate this problem, we propose a new association method that separately treats such difficult situations by modelling ambiguous assignments based on the differences in the distance matrix. Depending on the numbers of tracks and detections, for which the assignment task is determined ambiguous, different strategies to resolve these ambiguous situations are proposed. To further enhance the performance of our tracking framework, we introduce a camera motion-aware interpolation technique and make an adaptation to the motion model, which improves identity preservation. The effectiveness of our approach is demonstrated through extensive ablative experiments with different detection models. Moreover, the superiority w.r.t. other trackers is shown on the challenging MOT17 and MOT20 datasets, where state-of-the-art results are obtained. 

Modelling Ambiguous Assignments for Multi-Person Tracking in Crowds

Existing multi target multi camera tracking (MTMCT) datasets are small in terms of the number of identities and video length. The creation of new real world datasets is hard as privacy has to be guaranteed and the labeling is tedious. Therefore in the scope of this work a mod for GTA V to record a MTMCT dataset has been developed and used to record a simulated MTMCT dataset called Multi Camera Track Auto (MTA). The MTA dataset contains over 2,800 person identities, 6 cameras and a video length of over 100 minutes per camera. Additionally a MTMCT system has been implemented to provide a baseline for the created dataset. The system's pipeline consists of stages for person detection, person re-identification, single camera multi target tracking, track distance calculation, and track association. The track distance calculation comprises a weighted aggregation of the following distances: a single camera time constraint, a multi camera time constraint using overlapping camera areas, an appearance feature distance, a homography matching with pairwise camera homographies, and a linear prediction based on the velocity and the time difference of tracks. When using all partial distances, we were able to surpass the results of state-of-the-art single camera trackers by +13% IDF1 score. The MTA dataset, code, and baselines are available at github.com/schuar-iosb/mta-dataset.

/pdf/the-mta-dataset-for-multi-target-multi-camera-pedestrian-4btlg4aqyj.pdf

The MTA Dataset for Multi Target Multi Camera Pedestrian Tracking by Weighted Distance Aggregation

The search for persons based on their visual appearance is an important task of modern surveillance systems which can be supported by automatic person re-identification approaches. However, such approaches are generally image-based and thus require a query image as input. In cases where only a witness description is available the task turns into a cross-modal text-to-image search problem which requires specialized approaches. In this work we describe an approach for person search in video data based purely on attribute witness descriptions. We first develop an ensemble of classifiers for robust attribute classification. We then extend the approach to full person search by combining it with a person detector. Given an initial high-confidence match in the video we use temporal information to explore and return full person tracks. We evaluate our approach on the AVSS 2018 Soft Biometric Retrieval Challenge dataset. Our approach manages to find the correct person at rank-1 in 71.79% of all cases.

Attribute-based Person Retrieval and Search in Video Sequences

Automated re-identification (re-id) of vehicles is the foundation of many traffic analysis applications across camera networks, e.g. vehicle tracking, counting, or traffic density and flow estimation. The re-id task is made difficult by variations in lighting, viewpoint, image quality and similar vehicle models and colors that can occur across the network. These influences can cause a high visual appearance variation for the same vehicle while different vehicles may appear near identical under similar conditions. However, with a growing number of available datasets and well crafted deep learning models, much progress has been made. In this work we summarize the results of our participation in the NVIDIA AI City Challenge 2020 for vehicle reid. We address the re-id task by relying on well-proven design choices from the closely related person re-id literature. In addition to this, we explicitly address viewpoint and occlusions variation. The former is addressed by incorporating vehicle viewpoint classification results into our matching distance. The required viewpoint classifier is trained predominantly on simulated data and we show that it can be applied to real-world imagery with minimal domain adaptation. We address occlusion by relying on temporal attention scores which emphasize video frames that contain minimal occlusion. Finally, we further boost re-id accuracy by applying video-based re-ranking and an ensemble of complementary models. Our models, code, and simulated data is available at https://github.com/corner100/2020-aicitychallenge-IOSB-VeRi.

/pdf/large-scale-vehicle-re-identification-by-knowledge-transfer-3pq1pwyfde.pdf

Large Scale Vehicle Re-Identification by Knowledge Transfer From Simulated Data and Temporal Attention

Recognizing soft-biometric pedestrian attributes is essential in video surveillance and fashion retrieval. Recent works show promising results on single datasets. Nevertheless, the generalization ability of these methods under different attribute distributions, viewpoints, varying illumination, and low resolutions remains rarely understood due to strong biases and varying attributes in current datasets. To close this gap and support a systematic investigation, we present UPAR, the Unified Person Attribute Recognition Dataset. It is based on four well-known person attribute recognition datasets: PA100K, PETA, RAPv2, and Market1501. We unify those datasets by providing 3,3M additional annotations to harmonize 40 important binary attributes over 12 attribute categories across the datasets. We thus enable research on generalizable pedestrian attribute recognition as well as attribute-based person retrieval for the first time. Due to the vast variance of the image distribution, pedestrian pose, scale, and occlusion, existing approaches are greatly challenged both in terms of accuracy and efficiency. Furthermore, we develop a strong baseline for PAR and attribute-based person retrieval based on a thorough analysis of regularization methods. Our models achieve state-of-the-art performance in cross-domain and specialization settings on PA100k, PETA, RAPv2, Market1501-Attributes, and UPAR. We believe UPAR and our strong baseline will contribute to the artificial intelligence community and promote research on large-scale, generalizable attribute recognition systems. The dataset is available here: https://github.com/speckean/upar_dataset

UPAR: Unified Pedestrian Attribute Recognition and Person Retrieval

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