340 research outputs found
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields
This work presents a first evaluation of using spatio-temporal receptive
fields from a recently proposed time-causal spatio-temporal scale-space
framework as primitives for video analysis. We propose a new family of video
descriptors based on regional statistics of spatio-temporal receptive field
responses and evaluate this approach on the problem of dynamic texture
recognition. Our approach generalises a previously used method, based on joint
histograms of receptive field responses, from the spatial to the
spatio-temporal domain and from object recognition to dynamic texture
recognition. The time-recursive formulation enables computationally efficient
time-causal recognition. The experimental evaluation demonstrates competitive
performance compared to state-of-the-art. Especially, it is shown that binary
versions of our dynamic texture descriptors achieve improved performance
compared to a large range of similar methods using different primitives either
handcrafted or learned from data. Further, our qualitative and quantitative
investigation into parameter choices and the use of different sets of receptive
fields highlights the robustness and flexibility of our approach. Together,
these results support the descriptive power of this family of time-causal
spatio-temporal receptive fields, validate our approach for dynamic texture
recognition and point towards the possibility of designing a range of video
analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification
Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit
certain stationarity properties in time such as smoke, vegetation and fire. The
analysis of DT is important for recognition, segmentation, synthesis or
retrieval for a range of applications including surveillance, medical imaging
and remote sensing. Deep learning methods have shown impressive results and are
now the new state of the art for a wide range of computer vision tasks
including image and video recognition and segmentation. In particular,
Convolutional Neural Networks (CNNs) have recently proven to be well suited for
texture analysis with a design similar to a filter bank approach. In this
paper, we develop a new approach to DT analysis based on a CNN method applied
on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames
and temporal slices extracted from the DT sequences and combine their outputs
to obtain a competitive DT classifier. Our results on a wide range of commonly
used DT classification benchmark datasets prove the robustness of our approach.
Significant improvement of the state of the art is shown on the larger
datasets.Comment: 19 pages, 10 figure
ΠΠΠΠΠ ΠΠ’Π ΠΠΠΠΠΠΠ ΠΠΠΠΠΠΠ§ΠΠ‘ΠΠΠ₯ Π’ΠΠΠ‘Π’Π£Π
Recognizing dynamic patterns based on visual processing is significant for many applications such as remote monitoring for the prevention of natural disasters, e.g. forest fires, various types of surveillance, e.g. traffic monitoring, background subtraction in challenging environments, e.g. outdoor scenes with vegetation, homeland security applications and scientific studies of animal behavior. In the context of surveillance, recognizing dynamic patterns is of significance to isolate activities of interest (e.g. fire) from distracting background (e.g. windblown vegetation and changes in scene illumination).Methods: pattern recognition, computer vision.Results: This paper presents video based image processing algorithm with samples usually containing a cluttered background. According to the spatiotemporal features, four categorized groups were formulated. Dynamic texture recognition algorithm refers image objects to one of this group. Motion, color, facial, energy Laws and ELBP features are extracted for dynamic texture categorization. Classification based on boosted random forest.Practical relevance: Experimental results show that the proposed method is feasible and effective for video-based dynamic texture categorization. Averaged classification accuracy on the all video images is 95.2%.ΠΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠ° ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ: ΠΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠ΅ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΠΊΡΡΡΡ Π½Π° Π²ΠΈΠ΄Π΅ΠΎΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΡ
Π² Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ Π½Π°Ρ
ΠΎΠ΄ΠΈΡ Π²ΡΠ΅ Π±ΠΎΠ»Π΅Π΅ ΡΠΈΡΠΎΠΊΠΎΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π² ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π·ΡΠ΅Π½ΠΈΡ. ΠΠ°ΠΏΡΠΈΠΌΠ΅Ρ, ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠ΅ Π΄ΡΠΌΠ° ΠΈ ΠΏΠ»Π°ΠΌΠ΅Π½ΠΈ Π² ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π°, Π°Π½Π°Π»ΠΈΠ· Π°Π²ΡΠΎΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ°ΡΠΈΠΊΠ° ΠΏΡΠΈ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π΅ Π·Π°Π³ΡΡΠΆΠ΅Π½Π½ΠΎΡΡΠΈ Π΄ΠΎΡΠΎΠ³, ΠΈ Π² Π΄ΡΡΠ³ΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
. ΠΠΎΠΈΡΠΊ ΠΎΠ±ΡΠ΅ΠΊΡΠ° ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ° Π½Π° Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠΎΠ½Π΅ ΡΠ°ΡΡΠΎ Π±ΡΠ²Π°Π΅Ρ Π·Π°ΡΡΡΠ΄Π½Π΅Π½ Π·Π° ΡΡΠ΅Ρ ΠΏΠΎΡ
ΠΎΠΆΠΈΡ
ΡΠ΅ΠΊΡΡΡΡΠ½ΡΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² ΠΈΠ»ΠΈ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ Ρ ΡΠΎΠ½Π° ΠΈ ΠΈΡΠΊΠΎΠΌΠΎΠ³ΠΎ ΠΎΠ±ΡΠ΅ΠΊΡΠ°. Π ΡΠ²ΡΠ·ΠΈ Ρ ΡΡΠΈΠΌ Π²ΠΎΠ·Π½ΠΈΠΊΠ°Π΅Ρ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΠΊΡΡΡΡ Π΄Π»Ρ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ° Π½Π° Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠΎΠ½Π΅.ΠΠ΅ΡΠΎΠ΄Ρ: ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΠ΅ ΠΎΠ±ΡΠ°Π·ΠΎΠ², ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ΅ Π·ΡΠ΅Π½ΠΈΠ΅.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ: Π Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ° Π²ΠΈΠ΄Π΅ΠΎΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠΈΡ
ΠΎΠ±ΡΠ΅ΠΊΡΡ Ρ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ΠΌ Π½Π° Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠΎΠ½Π΅, ΡΠ°ΠΊΠΈΠ΅ ΠΊΠ°ΠΊ Π²ΠΎΠ΄Π°, ΡΡΠΌΠ°Π½, ΠΏΠ»Π°ΠΌΡ, ΡΠ΅ΠΊΡΡΠΈΠ»Ρ Π½Π° Π²Π΅ΡΡΡ ΠΈ Π΄Ρ. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΎΡΠ½Π΅ΡΠ΅Π½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² Π²ΠΈΠ΄Π΅ΠΎΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΊ ΠΎΠ΄Π½ΠΎΠΉ ΠΈΠ· ΡΠ΅ΡΡΡΠ΅Ρ
ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΡΡ
ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΉ. ΠΠ·Π²Π»Π΅ΠΊΠ°ΡΡΡΡ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΈ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ, ΡΠ²Π΅ΡΠΎΠ²ΡΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ, ΡΡΠ°ΠΊΡΠ°Π»ΡΠ½ΠΎΡΡΠΈ, ΡΠ½Π΅ΡΠ³Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΈ ΠΠ°ΡΠ°, ΡΡΡΠΎΡΡΡΡ ELBP-Π³ΠΈΡΡΠΎΠ³ΡΠ°ΠΌΠΌΡ. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠ° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ Π±ΡΡΡΠΈΠ½Π³ΠΎΠ²ΡΠΉ ΡΠ»ΡΡΠ°ΠΉΠ½ΡΠΉ Π»Π΅Ρ.ΠΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ: Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½ ΠΌΠ΅ΡΠΎΠ΄, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠΉ ΡΠ°Π·Π΄Π΅Π»ΠΈΡΡ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ΅ΠΊΡΡΡΡ Π½Π° ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΈ: ΠΏΠΎ ΡΠΈΠΏΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ (ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΈ Ρ
Π°ΠΎΡΠΈΡΠ½ΠΎΠ΅) ΠΈ ΡΠΈΠΏΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ° (ΠΏΡΠΈΡΠΎΠ΄Π½ΡΠ΅ ΠΈ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΡΠ΅). ΠΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°ΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π΄Π»Ρ ΠΎΡΠ½Π΅ΡΠ΅Π½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΊ ΡΠΎΠΉ ΠΈΠ»ΠΈ ΠΈΠ½ΠΎΠΉ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΈ. Π‘ΡΠ΅Π΄Π½ΡΡ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠΎΡΡΠ°Π²ΠΈΠ»Π° 95.2%
Unleashing the Power of VGG16: Advancements in Facial Emotion Recognization
In facial emotion detection, researchers are actively exploring effective methods to identify and understand facial expressions. This study introduces a novel mechanism for emotion identification using diverse facial photos captured under varying lighting conditions. A meticulously pre-processed dataset ensures data consistency and quality. Leveraging deep learning architectures, the study utilizes feature extraction techniques to capture subtle emotive cues and build an emotion classification model using convolutional neural networks (CNNs). The proposed methodology achieves an impressive 97% accuracy on the validation set, outperforming previous methods in terms of accuracy and robustness. Challenges such as lighting variations, head posture, and occlusions are acknowledged, and multimodal approaches incorporating additional modalities like auditory or physiological data are suggested for further improvement. The outcomes of this research have wide-ranging implications for affective computing, human-computer interaction, and mental health diagnosis, advancing the field of facial emotion identification and paving the way for sophisticated technology capable of understanding and responding to human emotions across diverse domains
Discriminatively Trained Latent Ordinal Model for Video Classification
We study the problem of video classification for facial analysis and human
action recognition. We propose a novel weakly supervised learning method that
models the video as a sequence of automatically mined, discriminative
sub-events (eg. onset and offset phase for "smile", running and jumping for
"highjump"). The proposed model is inspired by the recent works on Multiple
Instance Learning and latent SVM/HCRF -- it extends such frameworks to model
the ordinal aspect in the videos, approximately. We obtain consistent
improvements over relevant competitive baselines on four challenging and
publicly available video based facial analysis datasets for prediction of
expression, clinical pain and intent in dyadic conversations and on three
challenging human action datasets. We also validate the method with qualitative
results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text
overlap with arXiv:1604.0150
Efficient Human Activity Recognition in Large Image and Video Databases
Vision-based human action recognition has attracted considerable interest in recent research for its applications to video surveillance, content-based search, healthcare, and interactive games. Most existing research deals with building informative feature descriptors, designing efficient and robust algorithms, proposing versatile and challenging datasets, and fusing multiple modalities. Often, these approaches build on certain conventions such as the use of motion cues to determine video descriptors, application of off-the-shelf classifiers, and single-factor classification of videos. In this thesis, we deal with important but overlooked issues such as efficiency, simplicity, and scalability of human activity recognition in different application scenarios: controlled video environment (e.g.~indoor surveillance), unconstrained videos (e.g.~YouTube), depth or skeletal data (e.g.~captured by Kinect), and person images (e.g.~Flicker). In particular, we are interested in answering questions like (a) is it possible to efficiently recognize human actions in controlled videos without temporal cues? (b) given that the large-scale unconstrained video data are often of high dimension low sample size (HDLSS) nature, how to efficiently recognize human actions in such data? (c) considering the rich 3D motion information available from depth or motion capture sensors, is it possible to recognize both the actions and the actors using only the motion dynamics of underlying activities? and (d) can motion information from monocular videos be used for automatically determining saliency regions for recognizing actions in still images
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