87 research outputs found
Time-slice analysis of dyadic human activity
La reconnaissance d’activités humaines à partir de données vidéo est utilisée pour la surveillance ainsi que pour des applications d’interaction homme-machine. Le principal objectif est de classer les vidéos dans l’une des k classes d’actions à partir de vidéos entièrement observées. Cependant, de tout temps, les systèmes intelligents sont améliorés afin de prendre des décisions basées sur des incertitudes et ou des informations incomplètes. Ce besoin nous motive à introduire le problème de l’analyse de l’incertitude associée aux activités humaines et de pouvoir passer à un nouveau niveau de généralité lié aux problèmes d’analyse d’actions. Nous allons également présenter le problème de reconnaissance d’activités par intervalle de temps, qui vise à explorer l’activité humaine dans un intervalle de temps court. Il a été démontré que l’analyse par intervalle de temps est utile pour la caractérisation des mouvements et en général pour l’analyse de contenus vidéo. Ces études nous encouragent à utiliser ces intervalles de temps afin d’analyser l’incertitude associée aux activités humaines. Nous allons détailler à quel degré de certitude chaque activité se produit au cours de la vidéo. Dans cette thèse, l’analyse par intervalle de temps d’activités humaines avec incertitudes sera structurée en 3 parties. i) Nous présentons une nouvelle famille de descripteurs spatiotemporels optimisés pour la prédiction précoce avec annotations d’intervalle de temps. Notre représentation prédictive du point d’intérêt spatiotemporel (Predict-STIP) est basée sur l’idée de la contingence entre intervalles de temps. ii) Nous exploitons des techniques de pointe pour extraire des points d’intérêts afin de représenter ces intervalles de temps. iii) Nous utilisons des relations (uniformes et par paires) basées sur les réseaux neuronaux convolutionnels entre les différentes parties du corps de l’individu dans chaque intervalle de temps. Les relations uniformes enregistrent l’apparence locale de la partie du corps tandis que les relations par paires captent les relations contextuelles locales entre les parties du corps. Nous extrayons les spécificités de chaque image dans l’intervalle de temps et examinons différentes façons de les agréger temporellement afin de générer un descripteur pour tout l’intervalle de temps. En outre, nous créons une nouvelle base de données qui est annotée à de multiples intervalles de temps courts, permettant la modélisation de l’incertitude inhérente à la reconnaissance d’activités par intervalle de temps. Les résultats expérimentaux montrent l’efficience de notre stratégie dans l’analyse des mouvements humains avec incertitude.Recognizing human activities from video data is routinely leveraged for surveillance and human-computer interaction applications. The main focus has been classifying videos into one of k action classes from fully observed videos. However, intelligent systems must to make decisions under uncertainty, and based on incomplete information. This need motivates us to introduce the problem of analysing the uncertainty associated with human activities and move to a new level of generality in the action analysis problem. We also present the problem of time-slice activity recognition which aims to explore human activity at a small temporal granularity. Time-slice recognition is able to infer human behaviours from a short temporal window. It has been shown that temporal slice analysis is helpful for motion characterization and for video content representation in general. These studies motivate us to consider timeslices for analysing the uncertainty associated with human activities. We report to what degree of certainty each activity is occurring throughout the video from definitely not occurring to definitely occurring. In this research, we propose three frameworks for time-slice analysis of dyadic human activity under uncertainty. i) We present a new family of spatio-temporal descriptors which are optimized for early prediction with time-slice action annotations. Our predictive spatiotemporal interest point (Predict-STIP) representation is based on the intuition of temporal contingency between time-slices. ii) we exploit state-of-the art techniques to extract interest points in order to represent time-slices. We also present an accumulative uncertainty to depict the uncertainty associated with partially observed videos for the task of early activity recognition. iii) we use Convolutional Neural Networks-based unary and pairwise relations between human body joints in each time-slice. The unary term captures the local appearance of the joints while the pairwise term captures the local contextual relations between the parts. We extract these features from each frame in a time-slice and examine different temporal aggregations to generate a descriptor for the whole time-slice. Furthermore, we create a novel dataset which is annotated at multiple short temporal windows, allowing the modelling of the inherent uncertainty in time-slice activity recognition. All the three methods have been evaluated on TAP dataset. Experimental results demonstrate the effectiveness of our framework in the analysis of dyadic activities under uncertaint
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
Understanding Objects in the Visual World
One way to understand the visual world is by reasoning about the objects present in it: their type, their location, their similarities, their layout etc. Despite several successes, detailed recognition remains a challenging tasks for current computer vision systems. This dissertation focuses on building systems that improve on the state-of-the-art on several fronts. On one hand, we propose better representations of visual categories that enable more accurate reasoning about their properties. To learn such representations, we employ machine learning methods that leverage the power of big-data. On the other hand, we present solutions to make current frameworks more efficient without losing on performance.
The first part of the dissertation focuses on improvements in efficiency. We first introduce a fast automated mechanism for selecting a diverse set of discriminative filters and show that one can efficiently learn a universal model of filter "goodness" based on properties of the filter itself. As an alternative to the expensive evaluation of filters, which is often the bottleneck in many techniques, our method has the potential of dramatically altering the trade-off between the accuracy of a filter based method and the cost of training. Second, we present a method for linear dimensionality reduction which we call composite discriminant factor analysis (CDF). CDF searches for a discriminative but compact feature subspace in which the classifiers can be trained, leading to an order of magnitude saving in detection time.
In the second part, we focus on the problem of person re-identification, an important component of surveillance systems. We present a deep learning architecture that simultaneously learns features and computes their corresponding similarity metric. Given a pair of images as input, our network outputs a similarity value indicating whether the two input images depict the same person. We propose new layers which capture local relationships among mid-level features, produce a high-level summary of these relationships and spatially integrate them to give a holistic representation.
In the final part, we present a semantic object selection framework that uses natural language input to perform image editing. In the general context of interactive object segmentation, many of the methods that utilize user input (such as mouse clicks and mouse strokes) often require significant user intervention. In this work, we present a system with a far simpler input method: the user only needs to give the name of the desired object. For this problem we present a solution which borrows ideas from image retrieval, segmentation propagation, object localization and convolution neural networks
Actions and Attributes from Wholes and Parts
We investigate the importance of parts for the tasks of action and attribute
classification. We develop a part-based approach by leveraging convolutional
network features inspired by recent advances in computer vision. Our part
detectors are a deep version of poselets and capture parts of the human body
under a distinct set of poses. For the tasks of action and attribute
classification, we train holistic convolutional neural networks and show that
adding parts leads to top-performing results for both tasks. In addition, we
demonstrate the effectiveness of our approach when we replace an oracle person
detector, as is the default in the current evaluation protocol for both tasks,
with a state-of-the-art person detection system
Analyzing Complex Events and Human Actions in "in-the-wild" Videos
We are living in a world where it is easy to acquire videos of events ranging from private picnics to public concerts, and to share them publicly via websites such as YouTube. The ability of smart-phones to create these videos and upload them to the internet has led to an explosion of video data, which in turn has led to interesting research directions involving the analysis of ``in-the-wild'' videos. To process these types of videos, various recognition tasks such as pose estimation, action recognition, and event recognition become important in computer vision. This thesis presents various recognition problems and proposes mid-level models to address them.
First, a discriminative deformable part model is presented for the recovery of qualitative pose, inferring coarse pose labels (e:g: left, front-right, back), a task more robust to common confounding factors that hinder the inference of exact 2D or 3D joint locations. Our approach automatically selects parts that are predictive of qualitative pose and trains their appearance and deformation costs to best discriminate between qualitative poses. Unlike previous approaches, our parts are both selected and trained to improve qualitative pose discrimination and are shared by all the qualitative pose models. This leads to both increased accuracy and higher efficiency, since fewer parts models are evaluated for each image. In comparisons with two state-of-the-art approaches on a public dataset, our model shows superior performance.
Second, the thesis proposes the use of a robust pose feature based on part based human detectors (Poselets) for the task of action recognition in relatively unconstrained videos, i.e., collected from the web. This feature, based on the original poselets activation vector, coarsely models pose and its transitions over time. Our main contributions are that we improve the original feature's compactness and discriminability by greedy set cover over subsets of joint configurations, and incorporate it into a unified video-based action recognition framework. Experiments shows that the pose feature alone is extremely informative, yielding performance that matches most state-of-the-art approaches but only using our proposed improvements to its compactness and discriminability. By combining our pose feature with motion and shape, the proposed method outperforms state-of-the-art approaches on two public datasets.
Third, clauselets, sets of concurrent actions and their temporal relationships, are proposed and explored their application to video event analysis. Clauselets are trained in two stages. Initially, clauselet detectors that find a limited set of actions in particular qualitative temporal configurations based on Allen's interval relations is trained. In the second stage, the first level detectors are applied to training videos, and discriminatively learn temporal patterns between activations that involve more actions over longer durations and lead to improved second level clauselet models. The utility of clauselets is demonstrated by applying them to the task of ``in-the-wild'' video event recognition on the TRECVID MED 11 dataset. Not only do clauselets achieve state-of-the-art results on this task, but qualitative results suggest that they may also lead to semantically meaningful descriptions of videos in terms of detected actions and their temporal relationships.
Finally, the thesis addresses the task of searching for videos given text queries that are not known at training time, which typically involves zero-shot learning, where detectors for a large set of concepts, attributes, or objects parts are learned under the assumption that, once the search query is known, they can be combined to detect novel complex visual categories. These detectors are typically trained on annotated training data that is time-consuming and expensive to obtain, and a successful system requires many of them to generalize well at test time. In addition, these detectors are so general that they are not well-tuned to the specific query or target data, since neither is known at training. Our approach addresses the annotation problem by searching the web to discover visual examples of short text phrases. Top ranked search results are used to learn general, potentially noisy, visual phrase detectors. Given a search query and a target dataset, the visual phrase detectors are adapted to both the query and unlabeled target data to remove the influence of incorrect training examples or correct examples that are irrelevant to the search query. Our adaptation process exploits the spatio-temporal coocurrence of visual phrases that are found in the target data and which are relevant to the search query by iteratively refining both the visual phrase detectors and spatio-temporally grouped phrase detections (`clauselets'). Our approach is demonstrated on to the challenging TRECVID MED13 EK0 dataset and show that, using visual features alone, our approach outperforms state-of-the-art approaches that use visual, audio, and text (OCR) features
Multi-View Region Adaptive Multi-temporal DMM and RGB Action Recognition
Human action recognition remains an important yet challenging task. This work
proposes a novel action recognition system. It uses a novel Multiple View
Region Adaptive Multi-resolution in time Depth Motion Map (MV-RAMDMM)
formulation combined with appearance information. Multiple stream 3D
Convolutional Neural Networks (CNNs) are trained on the different views and
time resolutions of the region adaptive Depth Motion Maps. Multiple views are
synthesised to enhance the view invariance. The region adaptive weights, based
on localised motion, accentuate and differentiate parts of actions possessing
faster motion. Dedicated 3D CNN streams for multi-time resolution appearance
information (RGB) are also included. These help to identify and differentiate
between small object interactions. A pre-trained 3D-CNN is used here with
fine-tuning for each stream along with multiple class Support Vector Machines
(SVM)s. Average score fusion is used on the output. The developed approach is
capable of recognising both human action and human-object interaction. Three
public domain datasets including: MSR 3D Action,Northwestern UCLA multi-view
actions and MSR 3D daily activity are used to evaluate the proposed solution.
The experimental results demonstrate the robustness of this approach compared
with state-of-the-art algorithms.Comment: 14 pages, 6 figures, 13 tables. Submitte
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