LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning
We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework for generating crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior, flow, lighting conditions, viewpoint, noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by improving the accuracy of pedestrian detection and crowd behavior classification algorithms. LCrowdV would be released on the WWW.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationPedestrian DetectionScene UnderstandingSimilar Papers 제목 키워드 기반
A Framework for Video-Driven Crowd Synthesis
We present a framework for video-driven crowd synthesis. Motion vectors extracted from input crowd video are processed to compute global motion paths. These paths encode the dominant motions observed in the input video. …
Non-Volume Preserving-based Fusion to Group-Level Emotion Recognition on Crowd Videos
Group-level emotion recognition (ER) is a growing research area as the demands for assessing crowds of all sizes are becoming an interest in both the security arena as well as social media. This work extends the earlier …
Emotion RecognitionPredicting Future Pedestrian Motion in Video Sequences using Crowd Simulation
While human and group analysis have become an important area in last decades, some current and relevant applications involve to estimate future motion of pedestrians in real video sequences. This paper presents a method …
Motion EstimationHybrid Classifiers for Spatio-temporal Real-time Abnormal Behaviors Detection, Tracking, and Recognition in Massive Hajj Crowds
Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes. Challenges such as partial occlusions, blurring, large-number abnormal behavior, and camera viewing occur in large-scale crowds when dete…
object-detectionObject DetectionOptical Flow EstimationCROWD-IN-THE-LOOP: A Hybrid Approach for Annotating Semantic Roles
Crowdsourcing has proven to be an effective method for generating labeled data for a range of NLP tasks. However, multiple recent attempts of using crowdsourcing to generate gold-labeled training data for semantic role l…
Machine TranslationQuestion AnsweringSemantic Role Labeling