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Multi-Camera Vehicle Tracking Based on Occlusion-aware and Inter-vehicle Information

Multi-Camera Vehicle Tracking Based on Occlusion-aware and I...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liu, Yuming Zhang, Xiaochun Zhang, Bingzhen Zhang, Xiaoyong Wang, Sen Xu, Jianrong Shenzhen Urban Transport Planning Ctr Co Ltd Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With the demands of analyzing and predicting traffic flow for applications in smart cities, Multi-Target Multi-Camera vehicle Tracking(MTMCT) at the city scale has become a fundamental problem. The MTMCT is challenging due to the view variations, frequent occlusions, and similar vehicle models in the same camera. This work proposes an MTMCT framework based on occlusion-aware and intervehicle information that can effectively match vehicle tracklets. The occlusion-aware module segments the tracklets of an occluded and occluding vehicle pair. It recalculates the similarity of the complete tracklets, which can handle the occlusions and suppress false detections. This work proposes an inter-vehicle information module to improve the matching accuracy. The module can enhance the ability to distinguish similar vehicles under the same camera at different times. The proposed whole framework consists of four modules: (1) vehicle detection and feature extraction by re-identification models, (2) single-camera tracking (SCT) to produce initial tracklets with an occlusion-aware module, (3) tracklets similarity by inter-vehicle association, (4) clustering in adjacent cameras for multi-camera tracklets matching. The proposed method obtains IDF1 score of 0.8285 on the Track-1 multi-camera vehicle tracking task of the 2022 AI City Challenge.

关键词： computer vision Smart cities Vehicle detection conferences Cameras Feature extraction pattern recognition

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Cross-modal Target Retrieval for Tracking by Natural Language

Cross-modal Target Retrieval for Tracking by Natural Languag...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Li, Yihao Yu, Jun Cai, Zhongpeng Pan, Yuwen Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Tracking by natural language specification in a video is a challenging task in computer vision. Distinct from initializing the target state only by the bounding box in the first frame, language specification has a strong potential to assist visual object trackers to capture appearance variation and eliminate semantic ambiguity of the tracked object. In this paper, we carefully design a unified local-global-search framework from the perspective of cross-modal retrieval, including a local tracker, an adaptive retrieval switch module, and a target-specific retrieval module. The adaptive retrieval switch module aligns semantics from the visual signal and the lingual description of the target using three sub-modules, i.e., object-aware attention memory, part-aware cross-attention, and vision-language contrast, which achieve an automatic switch between local search and global search. When booting the global search mechanism, the target-specific retrieval module relocalizes the missing target in the image-wide range via an efficient vision-language guided proposal selector and target-text match. Numerous experimental results on three prevailing benchmarks show the effectiveness and generalization of our framework.

关键词： Visualization computer vision Target tracking Natural languages Semantics Switches Benchmark testing

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Probabilistic Compositional Embeddings for Multimodal Image Retrieval

Probabilistic Compositional Embeddings for Multimodal Image ...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Neculai, Andrei Chen, Yanbei Akata, Zeynep Univ Tubingen Tubingen Germany MPI Informat Saarbrucken Germany MPI Intelligent Syst Stuttgart Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Existing works in image retrieval often consider retrieving images with one or two query inputs, which do not generalize to multiple queries. In this work, we investigate a more challenging scenario for composing multiple multi-modal queries in image retrieval. Given an arbitrary number of query images and (or) texts, our goal is to retrieve target images containing the semantic concepts specified in multiple multimodal queries. To learn an informative embedding that can flexibly encode the semantics of various queries, we propose a novel multimodal probabilistic composer (MPC). Specifically, we model input images and texts as probabilistic embeddings, which can be further composed by a probabilistic composition rule to facilitate image retrieval with multiple multimodal queries. We propose a new benchmark based on the MS-COCO dataset and evaluate our model on various setups that compose multiple images and (or) text queries for multimodal image retrieval. Without bells and whistles, we show that our probabilistic model formulation significantly outperforms existing related methods on multimodal image retrieval while generalizing well to query with different amounts of inputs given in arbitrary visual and (or) textual modalities.

关键词： Visualization computer vision Codes conferences Computational modeling Image retrieval Semantics

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Panelformer: Sewing pattern Reconstruction from 2D Garment Images

Panelformer: Sewing Pattern Reconstruction from 2D Garment I...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Chen, Cheng-Hsiu Su, Jheng-Wei Hu, Min-Chun Yao, Chih-Yuan Chu, Hung-Kuo Natl Tsing Hua Univ Hsinchu Taiwan Natl Taiwan Univ Sci & Technol Taipei Taiwan

ISBN: (纸本)9798350318920;9798350318937

In this paper, we present a novel approach for reconstructing garment sewing patterns from 2D garment images. Our method addresses the challenge of handling occlusion in 2D images by leveraging the symmetric and correlated nature of garment panels. We introduce a transformer-based deep neural network called Panelformer that learns the parametric space of garment sewing patterns. The network comprises two components: the panel transformer and the stitch predictor. The panel transformer estimates the parametric panel shapes, including the occluded panels, by learning from the visible ones. The stitch predictor determines the stitching information among the predicted panels, enabling the reconstruction of the complete garment. To mitigate the overfitting problem caused by strong panel correlations, we propose two tailor-made data augmentation techniques: panel masking and garment mixing. These techniques generate a wider variety of panel combinations, enhancing the model's robustness and generalization capability. We evaluate the effectiveness of Panelformer using a synthetic dataset with diverse garment types. The experimental results demonstrate that our method outperforms competing baselines and achieves comparable performance to NeuralTailor, which operates on 3D point cloud data. This validates the efficacy of our approach in the context of garment sewing pattern reconstruction. By utilizing 2D images as input, our method expands the potential applications of garment modeling and offers easy accessibility to end users. Our code is available online(1).

关键词： 3D computer vision Algorithms Algorithms Image recognition and understanding

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Classification of Facial Expression In-the-Wild based on Ensemble of Multi-head Cross Attention Networks

Classification of Facial Expression In-the-Wild based on Ens...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Jeong, Jae Yeop Hong, Yeong-Gi Kim, Daun Jeong, Jin-Woo Jung, Yuchul Kim, Sang-Ho Seoul Natl Univ Sci & Technol Dept Data Sci Gongreung Ro 232 Seoul South Korea Kumoh Natl Inst Technol Daehak Ro 61 Gumi South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

How to build a system for robust classification and recognition of facial expressions has been one of the most important research issues for successful interactive computing applications. However, previous datasets and studies mainly focused on facial expression recognition in a controlled/lab setting, therefore, could hardly be generalized in a more practical and real-life environment. The Affective Behavior Analysis in-the-wild (ABAW) 2022 competition released a dataset consisting of various video clips of facial expressions in-the-wild. In this paper, we propose a method based on the ensemble of multi-head cross attention networks to address the facial expression classification task introduced in the ABAW 2022 competition. We built a uni-task approach for this task, achieving the average F1-score of 34.60 on the validation set and 33.77 on the test set, ranking second place on the final leaderboard.

关键词： Gold computer vision Face recognition conferences Estimation Multitasking Behavioral sciences

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Signature Detection, Restoration, and Verification: A Novel Chinese Document Signature Forgery Detection Benchmark

Signature Detection, Restoration, and Verification: A Novel ...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yan, Kaihong Zhang, Ying Tang, Haoran Ren, Chengkai Zhang, Jian Wang, Gaoang Wang, Hongwei Zhejiang Univ Zhejiang Univ Univ Illinois Urbana Champaign Inst Haining 314400 Zhejiang Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Offline signature forgery detection has attracted many researchers in recent years. In real situations, signatures should be detected from the signed documents and verified by the forgery detection system. There are many challenges in the pipeline. First, some signatures have low resolutions and are difficult to be detected. Second, the cropped signatures may contain irrelevant background context of the document, making the signature hard to be verified. Third, some forgery signatures are very similar to genuine ones, increasing the challenge of verification. In addition, most existing datasets do not cover all the pipeline tasks. Moreover, publicly available Chinese-based signature datasets are rare for research purposes. In this paper, we construct a novel Chinese document offline signature forgery detection benchmark, namely ChiSig, which includes all pipeline tasks, i.e., signature detection, restoration, and verification. Besides, we extensively compare different deep learning-based approaches in these three tasks. The results show that our proposed dataset can effectively provide solutions for constructing pipeline systems for Chinese document signature forgery detection.

关键词： computer vision conferences Pipelines Benchmark testing Forgery Task analysis

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Using Pure Pollen Species When Training a CNN to Segment Pollen Mixtures

Using Pure Pollen Species When Training a CNN to Segment Pol...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yang, Nana Joos, Victor Jacquemart, A. L. Buyens, Christel De Vleeschouwer, C. UCLouvain ICTEAM Inst Ottignies Belgium UCLouvain ELI Inst Ottignies Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recognizing the types of pollen grains and estimating their proportion in pollen mixture samples collected in a specific geographical area is important for agricultural, medical, and ecosystem research. Our paper adopts a convolutional neural network for the automatic segmentation of pollen species in microscopy images, and proposes an original strategy to train such network at reasonable manual annotation cost. Our approach is founded on a large dataset composed of pure pollen images. It first (semi-)manually segments foreground, i.e. pollen grains, and background in a fraction of those images, and use the resulting annotated dataset to train a universal pollen segmentation CNN. In the second step, this model is used to automatically segment a large number of additional pure pollen images, so as to supervise the training of a pollen species segmentation model. Despite the fact that it has been trained from pure images only, the model is shown to provide accurate segmentation of species in pollen mixtures. Our experiments also demonstrate that dedicating a model to the segmentation of a subset of the available pure pollen species makes it possible to train a bin pollen class, corresponding to pollen species that are not in the subset of species recognized by the model. This strategy is useful to cope with unexpected species in a mixture.

关键词： Training Image segmentation computer vision Costs Computational modeling Microscopy pattern recognition

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Continually Learning Self-Supervised Representations with Projected Functional Regularization

Continually Learning Self-Supervised Representations with Pr...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Gomez-Villa, Alex Twardowski, Bartlomiej Yu, Lu Bagdanov, Andrew D. van de Weijer, Joost Univ Autonoma Barcelona Comp Vis Ctr Barcelona Spain Tianjin Univ Technol Sch Comp Sci & Engn Tianjin Peoples R China Univ Florence MICC Florence Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent self-supervised learning methods are able to learn high-quality image representations and are closing the gap with supervised approaches. However, these methods are unable to acquire new knowledge incrementally they are, in fact, mostly used only as a pre-training phase over IID data. In this work we investigate self-supervised methods in continual learning regimes without any replay mechanism. We show that naive functional regularization, also known as feature distillation, leads to lower plasticity and limits continual learning performance. Instead, we propose Projected Functional Regularization in which a separate temporal projection network ensures that the newly learned feature space preserves information of the previous one, while at the same time allowing for the learning of new features. This prevents forgetting while maintaining the plasticity of the learner. Comparison with other incremental learning approaches applied to self-supervision demonstrates that our method obtains competitive performance in different scenarios and on multiple datasets.

关键词： computer vision conferences Self-supervised learning Image representation pattern recognition

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The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark

The multi-modal universe of fast-fashion: the Visuelle 2.0 b...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Skenderi, Geri Joppi, Christian Denitto, Matteo Scarpa, Berniero Cristani, Marco Univ Verona Verona Italy Humatics Srl Verona Italy Nuna Lie Srl Rome Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present Visuelle 2.0, the first dataset useful for facing diverse prediction problems that a fast-fashion company has to manage routinely. Furthermore, we demonstrate how the use of computer vision is substantial in this scenario. Visuelle 2.0 contains data for 6 seasons / 5355 clothing products of Nuna Lie(1), a famous Italian company with hundreds of shops located in different areas within the country. In particular, we focus on a specific prediction problem, namely short-observation new product sale forecasting (SO-fore). SO-fore assumes that the season has started and a set of new products is on the shelves of the different stores. The goal is to forecast the sales for a particular horizon, given a short, available past (few weeks), since no earlier statistics are available. To be successful, SO-fore approaches should capture this short past and exploit other modalities or exogenous data. To these aims, Visuelle 2.0 is equipped with disaggregated data at the item-shop level and multi-modal information for each clothing item, allowing computer vision approaches to come into play. The main message that we deliver is that the use of image data with deep networks boosts performances obtained when using the time series in long-term forecasting scenarios, ameliorating the WAPE by 8.2% and the MAE by 7.7%. The dataset is available at : https://***/forecasting/visuelle.

关键词： computer vision conferences Clothing Time series analysis Companies Benchmark testing pattern recognition

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Multi-encoder Network for Parameter Reduction of a Kernel-based Interpolation Architecture

Multi-encoder Network for Parameter Reduction of a Kernel-ba...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Khalifeh, Issa Blanch, Marc Gorriz Izquierdo, Ebroul Mrak, Marta British Broadcasting Corp London W12 7TQ England Queen Mary Univ London London E1 4NS England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Video frame interpolation involves the synthesis of new frames from existing ones. Convolutional neural networks (CNNs) have been at the forefront of the recent advances in this field. One popular CNN-based approach involves the application of generated kernels to the input frames to obtain an interpolated frame. Despite all the benefits interpolation methods offer, many of these networks require a lot of parameters, with more parameters meaning a heavier computational burden. Reducing the size of the model typically impacts performance negatively. This paper presents a method for parameter reduction for a popular flow-less kernel-based network (Adaptive Collaboration of Flows). Through our technique of removing the layers that require the most parameters and replacing them with smaller encoders, we reduce the number of parameters of the network and even achieve better performance compared to the original method. This is achieved by deploying rotation to force each individual encoder to learn different features from the input images. Ablations are conducted to justify design choices and an evaluation on how our method performs on full-length videos is presented.

关键词： Training Interpolation computer vision conferences Force computer architecture pattern recognition

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