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An open question in facial landmark localization in video is whether one should perform tracking or tracking-by-detection (i.e. face alignment). Tracking produces fittings of high accuracy but is prone to drifting. Tracking-by-detection is drift-free but results in low accuracy fittings. To provide a solution to this problem, we describe the very first, to the best of our knowledge, synergistic approach...
Current state-of-the-art approaches for spatio-temporal action localization rely on detections at the frame level that are then linked or tracked across time. In this paper, we leverage the temporal continuity of videos instead of operating at the frame level. We propose the ACtion Tubelet detector (ACT-detector) that takes as input a sequence of frames and outputs tubelets, i.e., sequences of bounding...
In this paper, we analyze the performances of the Cell Averaging based on Lookup Tables (CA-LT) detector considering distributed targets embedded in Pareto distributed clutter. Assuming that the clutter parameters are not a priori known, the detector's structure is associated to an estimation approach that estimates the clutter parameters. The latter are used to select the suitable threshold factor...
Different types of traffic signs has different colors and shapes located in uncontrolled traffic environments. The detection of different types of traffic signs is a difficult problem in pattern recognition and computer vision. In our study, a region of interest (ROI) extraction method is proposed to extract ROI using color contrast in local regions. We utilize the high contrast in local regions to...
In this paper, we propose to analyze the performances of the Multiple pulse Cell Averaging based on Lookup Tables detector (M-pulse CA-LT), considering range spread targets embedded in Non Gaussian clutter. Under the assumption of Weibull clutter samples with unknown parameters, we introduce the pulse-to-pulse Moments (MOM) estimation approach. The latter estimates the shape and the scale parameter...
This paper is devoted to traffic sign recognition problem in real time. The recognition process consists of three steps. The first step is a search of image parts which probably contain a traffic sign. The second step is about parts extraction and simple classification by shape. The last step consists of classification of extracted parts with previously learned multilayered neural net. The results...
Learning automatically the structure of object categories remains an important open problem in computer vision. In this paper, we propose a novel unsupervised approach that can discover and learn landmarks in object categories, thus characterizing their structure. Our approach is based on factorizing image deformations, as induced by a viewpoint change or an object deformation, by learning a deep...
One of the most important problems in radar detection design is the maintaining of Constant False Alarm (CFAR). In the context of non coherent detection for high resolution radars with low grazing angle, we propose a new decision rule that achieves the CFAR property with respect to a class of clutter models. This paper demonstrates that the new proposed detector is CFAR and robust under the assumption...
Nowadays the task of tracking pedestrians is often addressed within a tracking-by-detection framework, which in most cases entails that the position of each target has been detected before tracking begins. However in some cases, a pedestrian who is being tracked may be obscured by other targets or obstacles, and during this period they may change their trajectory or speed (track drift), and sometimes...
This paper addresses the problem of pedestrian detection in high-density crowd images, characterized by strong homogeneity and clutter. We propose an evidential fusion algorithm which is able to exploit multiple detectors based on different gradient, texture and orientation descriptors. The evidential framework allows us to model the spatial imprecision arising from each of the detectors. A first...
We present a method of predictive reconstructing connections between parts of object outlines in images. The method was developed mainly to analyze microscopic medical images but is applicable to other types of images. Examined objects in such images are highly transparent, moreover close objects can overlap each other. Thus, segmentation and separation of such objects can be difficult. Another frequently...
Automatic blobs detection constitutes a basic but difficult problem. In this work a new fast blobs detection technique based on a scale-space representation of the original image, is proposed. The scale-space representation is constructed by using a new simplified form of the Fast Radial Symmetry Transform to precisely detect the essential blobs. From the experiments we have conducted, the proposed...
Finding what is and what is not a salient object can be helpful in developing better features and models in salient object detection (SOD). In this paper, we investigate the images that are selected and discarded in constructing a new SOD dataset and find that many similar candidates, complex shape and low objectness are three main attributes of many non-salient objects. Moreover, objects may have...
Robust covariant local feature detectors are important for detecting local features that are (1) discriminative of the image content and (2) can be repeatably detected at consistent locations when the image undergoes diverse transformations. Such detectors are critical for applications such as image search and scene reconstruction. Many learning-based local feature detectors address one of these two...
This paper addresses the problem of 3D human pose estimation from a single image. We follow a standard two-step pipeline by first detecting the 2D position of the N body joints, and then using these observations to infer 3D pose. For the first step, we use a recent CNN-based detector. For the second step, most existing approaches perform 2N-to-3N regression of the Cartesian joint coordinates. We show...
We present a method for localizing facial keypoints on animals by transferring knowledge gained from human faces. Instead of directly finetuning a network trained to detect keypoints on human faces to animal faces (which is sub-optimal since human and animal faces can look quite different), we propose to first adapt the animal images to the pre-trained human detection network by correcting for the...
Regression based facial landmark detection methods usually learns a series of regression functions to update the landmark positions from an initial estimation. Most of existing approaches focus on learning effective mapping functions with robust image features to improve performance. The approach to dealing with the initialization issue, however, receives relatively fewer attentions. In this paper,...
Constrained Local Models (CLMs) are a well-established family of methods for facial landmark detection. However, they have recently fallen out of favor to cascaded regressionbased approaches. This is in part due to the inability of existing CLM local detectors to model the very complex individual landmark appearance that is affected by expression, illumination, facial hair, makeup, and accessories...
Traditional point tracking algorithms such as the KLT use local 2D information aggregation for feature detection and tracking, due to which their performance degrades at the object boundaries that separate multiple objects. Recently, CoMaL Features have been proposed that handle such a case. However, they proposed a simple tracking framework where the points are re-detected in each frame and matched...
We present a framework for robust face detection and landmark localisation of faces in the wild, which has been evaluated as part of `the 2nd Facial Landmark Localisation Competition'. The framework has four stages: face detection, bounding box aggregation, pose estimation and landmark localisation. To achieve a high detection rate, we use two publicly available CNN-based face detectors and two proprietary...
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