Online pedestrian tracking with multi-stage re-identification

Jiang, Yifan; Shin, Hyunhak; Ju, Jaeyong; Ko, Hanseok

doi:10.1109/avss.2017.8078505

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…The score of each object can be gotten according formula (1). ( 0 x , 0 y , 0 l ) is used to describe the location and size of the aim point.…”

Section: Related Technologiesmentioning

confidence: 99%

See 1 more Smart Citation

Study on the Specified Target Traceability in Multi-camera

Zhu¹,

Yin²,

Ding³

et al. 2018

dtcse

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Abstract. In view of the tracing of the pedestrian history trajectory, a counter-tracking method based on multi-camera is proposed. Firstly, a basic database including many attributes such as video frame rate and perspective relationship is established. Secondly, all of the cameras in the environment are calibrated and the relationships among them are constructed. Various features obtained by ResNet neural network are used to improve the accuracy of recalibration for the processed camera data. Finally, using the mean-shift algorithm realizes target tracking and traceability in reverse-time. Compared with ASMS, ColorKCF and Staple algorithm, experiments in the paper show that the proposed method has some advantages in accuracy and loss rate of target tracking, which is effective in practical application.

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“…The score of each object can be gotten according formula (1). ( 0 x , 0 y , 0 l ) is used to describe the location and size of the aim point.…”

Section: Related Technologiesmentioning

confidence: 99%

“…The difficulties that will be met in the task are move-then-stop [1], occlusion, illumination variation, deformation, moving background, background clutter, fast motion and so on. At the beginning, the research is based on tracking single object, but nowadays multi-object tracking is becoming a new topic [2].…”

Section: Introductionmentioning

confidence: 99%

Study on the Specified Target Traceability in Multi-camera

Zhu¹,

Yin²,

Ding³

et al. 2018

dtcse

View full text Add to dashboard Cite

show abstract

“…Machine learning-based person re-identification systems have become increasingly important for a variety of security and safety tasks. These systems typically employ a mapping function that learns to embed images into a compact Euclidean space, often a unit sphere [3,4]. The goal of this embedding is to ensure that images of the same person are mapped to adjacent feature points, while images of different individuals are mapped to distant feature points.…”

Section: Introductionmentioning

confidence: 99%

An Integrated vMF Embedding and VOS Framework for Robust Pedestrian Re-Identification and Out-of-Distribution Detection

Bouzid¹,

Sierra-Sosa²,

Elmaghraby³

2023

Preprint

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In recent years, pedestrian re-identification has gained a lot of interest due to its importance for many purposes such as security and safety. Many types of solutions have been proposed to solve this problem, where the majority are based on a features extraction such Convolution Neural Networks (CNNs). These approaches assume that only the identities that are in the training data can be recognized. The pedestrians in the training data are called In distribution (ID). However, in real world scenarios, new pedestrians and objects can appear in the scene and the model should detect them as Out Of Distribution (OOD). In our previous study , we proposed a pedestrian re-identification based on von-Mises Fisher (vMF) distribution. Each identity is embedded in the unit sphere as a compact vMF distribution far from other identities distributions. The embedding is done through a base CNN. Recently, proposed a framework called Virtual Outlier Synthetic (VOS), that detects OOD based on synthesising virtual outlier in the embedding space in an online manner. Their approach assumes that the samples from the same object maps to a compact space. This assumption aligns with the vMF based approach. Therefore, in this paper, we revisited the vMF approach and merged with VOS in order to detect OOD data points. We present our approach to merge both frameworks. We conducted several experiments to evaluate our proposed framework. Results showed that our framework was able to detect new pedestrian that do not exist in the training data in the inference phase. It also slightly helped to improve the re-identification performance.

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“…H UMAN action recognition is crucial in various applications ranging from video surveillance and humancomputer interaction to video understanding [1]- [5]. In recent years, skeleton-based human action recognition has attracted significant research attention due to the development of low-cost motion sensors and their robustness when faced with complicated environments such as background clutter and changes in illumination.…”

Section: Introductionmentioning

confidence: 99%

Pose-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition

Chen

Jiang

2022

IEEE Access

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Graph convolutional networks (GCN), which can model the human body skeletons as spatial and temporal graphs, have shown remarkable potential in skeleton-based action recognition. However, in the existing GCN-based methods, graph-structured representation of the human skeleton makes it difficult to be fused with other modalities, especially in the early stages. This may limit their scalability and performance in action recognition tasks. In addition, the pose information, which naturally contains informative and discriminative clues for action recognition, is rarely explored together with skeleton data in existing methods. In this work, we proposed pose-guided GCN (PG-GCN), a multi-modal framework for high-performance human action recognition. In particular, a multi-stream network is constructed to simultaneously explore the robust features from both the pose and skeleton data, while a dynamic attention module is designed for early-stage feature fusion. The core idea of this module is to utilize a trainable graph to aggregate features from the skeleton stream with that of the pose stream, which leads to a network with more robust feature representation ability. Extensive experiments show that the proposed PG-GCN can achieve state-of-the-art performance on the NTU RGB+D 60 and NTU RGB+D 120 datasets.

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Online pedestrian tracking with multi-stage re-identification

Cited by 7 publications

References 23 publications

Study on the Specified Target Traceability in Multi-camera

Study on the Specified Target Traceability in Multi-camera

An Integrated vMF Embedding and VOS Framework for Robust Pedestrian Re-Identification and Out-of-Distribution Detection

Pose-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition

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