Deep learning for person re-identification: A survey and outlook
Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-
overlapping cameras. With the advancement of deep neural networks and increasing …
overlapping cameras. With the advancement of deep neural networks and increasing …
A review of object detection based on deep learning
With the rapid development of deep learning techniques, deep convolutional neural
networks (DCNNs) have become more important for object detection. Compared with …
networks (DCNNs) have become more important for object detection. Compared with …
Strongsort: Make deepsort great again
Y Du, Z Zhao, Y Song, Y Zhao, F Su… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Recently, Multi-Object Tracking (MOT) has attracted rising attention, and accordingly,
remarkable progresses have been achieved. However, the existing methods tend to use …
remarkable progresses have been achieved. However, the existing methods tend to use …
Bytetrack: Multi-object tracking by associating every detection box
Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in
videos. Most methods obtain identities by associating detection boxes whose scores are …
videos. Most methods obtain identities by associating detection boxes whose scores are …
Simvp: Simpler yet better video prediction
Abstract From CNN, RNN, to ViT, we have witnessed remarkable advancements in video
prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated …
prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated …
Ota: Optimal transport assignment for object detection
Recent advances in label assignment in object detection mainly seek to independently
define positive/negative training samples for each ground-truth (gt) object. In this paper, we …
define positive/negative training samples for each ground-truth (gt) object. In this paper, we …
Transtrack: Multiple object tracking with transformer
In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple
object tracking problems. TransTrack leverages the transformer architecture, which is an …
object tracking problems. TransTrack leverages the transformer architecture, which is an …
Fairmot: On the fairness of detection and re-identification in multiple object tracking
Multi-object tracking (MOT) is an important problem in computer vision which has a wide
range of applications. Formulating MOT as multi-task learning of object detection and re-ID …
range of applications. Formulating MOT as multi-task learning of object detection and re-ID …
A survey of deep learning techniques for autonomous driving
The last decade witnessed increasingly rapid progress in self‐driving vehicle technology,
mainly backed up by advances in the area of deep learning and artificial intelligence (AI) …
mainly backed up by advances in the area of deep learning and artificial intelligence (AI) …
Chained-tracker: Chaining paired attentive regression results for end-to-end joint multiple-object detection and tracking
Abstract Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-
detection paradigm to conduct object detection, feature extraction and data association …
detection paradigm to conduct object detection, feature extraction and data association …