Real-time AdaBoost cascade face tracker based on likelihood map and optical flow

• URL: http://arxiv.org/abs/2210.13885v1
• Date: Tue, 25 Oct 2022 10:15:07 GMT
• Title: Real-time AdaBoost cascade face tracker based on likelihood map and optical flow
• Authors: Andreas Ranftl, Fernando Alonso-Fernandez, Stefan Karlsson, Josef Bigun
• Abstract summary: The authors present a novel face tracking approach where optical flow information is incorporated into a modified version of the Viola Jones detection algorithm. In the original algorithm, detection is static, as information from previous frames is not considered. The proposed tracker preserves information about the number of classification stages passed by each window.
• Score: 59.17685450892182
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The authors present a novel face tracking approach where optical flow information is incorporated into a modified version of the Viola Jones detection algorithm. In the original algorithm, detection is static, as information from previous frames is not considered. In addition, candidate windows have to pass all stages of the classification cascade, otherwise they are discarded as containing no face. In contrast, the proposed tracker preserves information about the number of classification stages passed by each window. Such information is used to build a likelihood map, which represents the probability of having a face located at that position. Tracking capabilities are provided by extrapolating the position of the likelihood map to the next frame by optical flow computation. The proposed algorithm works in real time on a standard laptop. The system is verified on the Boston Head Tracking Database, showing that the proposed algorithm outperforms the standard Viola Jones detector in terms of detection rate and stability of the output bounding box, as well as including the capability to deal with occlusions. The authors also evaluate two recently published face detectors based on convolutional networks and deformable part models with their algorithm showing a comparable accuracy at a fraction of the computation time.

Related papers

• A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent [57.64826450787237]
  We show how to analyze the behavior of distributed gradient descent algorithms in the presence of adversarial corruptions. We show how to use ideas from (lazy) mirror descent to design a corruption-tolerant distributed optimization algorithm. Experiments based on linear regression, support vector classification, and softmax classification on the MNIST dataset corroborate our theoretical findings.
  arXiv  Detail & Related papers  (2024-07-19T08:29:12Z)
• MapTrack: Tracking in the Map [14.991113420276767]
  Multi-Object Tracking (MOT) aims to maintain stable and uninterrupted trajectories for each target. Most state-of-the-art approaches first detect objects in each frame and then implement data association between new detections and existing tracks. We propose a new framework comprising of three lightweight and plug-and-play algorithms: the probability map, the prediction map, and the covariance adaptive Kalman filter.
  arXiv  Detail & Related papers  (2024-02-20T12:35:23Z)
• Dense Optical Tracking: Connecting the Dots [82.79642869586587]
  DOT is a novel, simple and efficient method for solving the problem of point tracking in a video. We show that DOT is significantly more accurate than current optical flow techniques, outperforms sophisticated "universal trackers" like OmniMotion, and is on par with, or better than, the best point tracking algorithms like CoTracker.
  arXiv  Detail & Related papers  (2023-12-01T18:59:59Z)
• An Explainable Model-Agnostic Algorithm for CNN-based Biometrics Verification [55.28171619580959]
  This paper describes an adaptation of the Local Interpretable Model-Agnostic Explanations (LIME) AI method to operate under a biometric verification setting.
  arXiv  Detail & Related papers  (2023-07-25T11:51:14Z)
• Fast Key Points Detection and Matching for Tree-Structured Images [4.929206987094714]
  This paper offers a new authentication algorithm based on image matching of nano-resolution visual identifiers with tree-shaped patterns. The proposed algorithm is applicable to a variety of tree-structured image matching, but our focus is on dendrites, recently-developed visual identifiers.
  arXiv  Detail & Related papers  (2022-11-07T00:22:56Z)
• Class agnostic moving target detection by color and location prediction of moving area [11.326363150470204]
  Moving target detection plays an important role in computer vision. Recent algorithms such as deep learning-based convolutional neural networks have achieved high accuracy and real-time performance. We propose a model free moving target detection algorithm.
  arXiv  Detail & Related papers  (2021-06-24T12:34:58Z)
• DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows [52.31831255787147]
  We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA) Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution. Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
  arXiv  Detail & Related papers  (2021-05-30T22:07:13Z)
• Fast Facial Landmark Detection and Applications: A Survey [0.0]
  We focus on approaches that have led to a significant increase in quality over the past few years on datasets with large pose and emotion variability. We summarize the improvements into categories, provide quality comparison on difficult and modern in-the-wild datasets: 300-W, AFLW, WFLW, COFW.
  arXiv  Detail & Related papers  (2021-01-12T09:40:40Z)
• Dense Label Encoding for Boundary Discontinuity Free Rotation Detection [69.75559390700887]
  This paper explores a relatively less-studied methodology based on classification. We propose new techniques to push its frontier in two aspects. Experiments and visual analysis on large-scale public datasets for aerial images show the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-11-19T05:42:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.