Robust Face Tracking using Multiple Appearance Models and Graph Relational Learning
This paper addresses the problem of appearance matching across different challenges while doing visual face tracking in real-world scenarios. In this paper, FaceTrack is proposed that utilizes multiple appearance models with its long-term and short-term appearance memory for efficient face tracking. It demonstrates robustness to deformation, in-plane and out-of-plane rotation, scale, distractors and background clutter. It capitalizes on the advantages of the tracking-by-detection, by using a face detector that tackles drastic scale appearance change of a face. The detector also helps to reinitialize FaceTrack during drift. A weighted score-level fusion strategy is proposed to obtain the face tracking output having the highest fusion score by generating candidates around possible face locations. The tracker showcases impressive performance when initiated automatically by outperforming many state-of-the-art trackers, except Struck by a very minute margin: 0.001 in precision and 0.017 in success respectively.
Code (1)
Tasks
Relational ReasoningSimilar Papers 제목 키워드 기반
Fast Online and Relational Tracking
To overcome challenges in multiple object tracking task, recent algorithms use interaction cues alongside motion and appearance features. These algorithms use graph neural networks or transformers to extract interaction …
Multiple Object TrackingObject TrackingPairwise Relational Networks using Local Appearance Features for Face Recognition
We propose a new face recognition method, called a pairwise relational network (PRN), which takes local appearance features around landmark points on the feature map, and captures unique pairwise relations with the same …
Face IdentificationFace RecognitionFace VerificationFaceQSORT: a Multi-Face Tracking Method based on Biometric and Appearance Features
Tracking multiple faces is a difficult problem, as there may be partially occluded or lateral faces. In multiple face tracking, association is typically based on (biometric) face features. However, the models used to ext…
Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects with Video Tracking Enabled Memory Models
Robots need to have a memory of previously observed, but currently occluded objects to work reliably in realistic environments. We investigate the problem of encoding object-oriented memory into a multi-object manipulati…
ObjectObject DiscoveryTGCN: Time Domain Graph Convolutional Network for Multiple Objects Tracking
Multiple object tracking is to give each object an id in the video. The difficulty is how to match the predicted objects and detected objects in same frames. Matching features include appearance features, location featur…
Multiple Object TrackingObjectObject Tracking