STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction

Zhang, Zhishuai; Gao, Jiyang; Mao, Junhua; Liu, Yukai; Anguelov, Dragomir; Li, Congcong

doi:10.1109/cvpr42600.2020.01136

Cited by 64 publications

(32 citation statements)

References 23 publications

(44 reference statements)

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“…A new trend in representation of the acting agents for neural network models, were graph models [102][103][104][105][106][107][108][109]. Describing the connections between objects in a graph structure in opposition to an occupancy grid, is a huge advantage if there are only sparse connections between the objects.…”

Section: Gnns Attention and New Use Casesmentioning

confidence: 99%

A Review on Scene Prediction for Automated Driving

Stockem

Krüger

Stolpe

et al. 2022

Physics

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Towards the aim of mastering level 5, a fully automated vehicle needs to be equipped with sensors for a 360∘ surround perception of the environment. In addition to this, it is required to anticipate plausible evolutions of the traffic scene such that it is possible to act in time, not just to react in case of emergencies. This way, a safe and smooth driving experience can be guaranteed. The complex spatio-temporal dependencies and high dynamics are some of the biggest challenges for scene prediction. The subtile indications of other drivers’ intentions, which are often intuitively clear to the human driver, require data-driven models such as deep learning techniques. When dealing with uncertainties and making decisions based on noisy or sparse data, deep learning models also show a very robust performance. In this survey, a detailed overview of scene prediction models is presented with a historical approach. A quantitative comparison of the model results reveals the dominance of deep learning methods in current state-of-the-art research in this area, leading to a competition on the cm scale. Moreover, it also shows the problem of inter-model comparison, as many publications do not use standardized test sets. However, it is questionable if such improvements on the cm scale are actually necessary. More effort should be spent in trying to understand varying model performances, identifying if the difference is in the datasets (many simple situations versus many corner cases) or actually an issue of the model itself.

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Section: Gnns Attention and New Use Casesmentioning

confidence: 99%

A Review on Scene Prediction for Automated Driving

Stockem

Krüger

Stolpe

et al. 2022

Physics

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show abstract

“…Future predictions are highly uncertain because of the unknown intents and behaviors of the agents [14,33,17,21,28,38]. In the field of autonomous driving, to model the high degree of multimodality, implicitly using latent variables is a popular approach [15,35,27,29].…”

Section: Related Workmentioning

confidence: 99%

DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets

Gu¹,

Sun²,

Zhao³

2021

Preprint

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Due to the stochasticity of human behaviors, predicting the future trajectories of road agents is challenging for autonomous driving. Recently, goal-based multi-trajectory prediction methods are proved to be effective, where they first score over-sampled goal candidates and then select a final set from them. However, these methods usually involve goal predictions based on sparse pre-defined anchors and heuristic goal selection algorithms. In this work, we propose an anchor-free and end-to-end trajectory prediction model, named DenseTNT, that directly outputs a set of trajectories from dense goal candidates. In addition, we introduce an offline optimization-based technique to provide multi-future pseudo-labels for our final online model. Experiments show that DenseTNT achieves state-of-the-art performance, ranking 1 st on the Argoverse motion forecasting benchmark and being the 1 st place winner of the 2021 Waymo Open Dataset Motion Prediction Challenge.

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“…The simple output of cutting-edge Crowd Human identification algorithms is provided. While significant progress has been achieved in pedestrian recognition [28], [29], identification in congested settings remains difficult. The conventional Non-Maximum Suppression (NMS) [30] has significant problems due to the severe occlusion of pedestrians.…”

Section: Pedestrian Detectionmentioning

confidence: 99%

Object Detection in Deep Surveillance

Thakur

Nagrath

Jain

et al. 2021

Preprint

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Object detection is a key ability required by most computer visions and surveillance applications. Pedestrian detection is a key problem in surveillance, with several applications such as person identification, person count and tracking. The number of techniques to identifying pedestrians in images has gradually increased in recent years, even with the significant advances in the state-of-the-art deep neural network-based framework for object detection models. The research in the field of object detection and image classification has made a stride in the level of accuracy greater than 99% and the level of granularity. A powerful Object detector, specifically designed for high-end surveillance applications, is needed that will not only position the bounding box and label it but will also return their relative positions. The size of these bounding boxes can vary depending on the object and it interacts with the physical world. To address these requirements, an extensive evaluation of the state-of-the-art algorithms has been performed in this paper. The work presented in this paper performs detections on MOT20 dataset using various algorithms and testing on a custom dataset recorded in our organization premises using an Unmanned Aerial Vehicle (UAV). The experimental analysis has been performed on Faster-RCNN, SSD and YOLO models. The Yolov5 model is found to outperform all the other models with 61% precision and 44% of F measure value.

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STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction

Cited by 64 publications

References 23 publications

A Review on Scene Prediction for Automated Driving

A Review on Scene Prediction for Automated Driving

DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets

Object Detection in Deep Surveillance

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