An Approach to Detect Crowd Panic Behavior using Flow-based Feature

With the purpose of achieving automated detection of crowd abnormal behavior in public, this paper discusses the category of typical crowd and individual behaviors and their patterns. Popular image features for abnormal behavior detection are also introduced, including global flow based features such as optical flow, and local spatio-temporal based features such as Spatio-temporal Volume (STV). After reviewing some relative abnormal behavior detection algorithms, a brandnew approach to detect crowd panic behavior has been proposed based on optical flow features in this paper. During the experiments, all panic behaviors are successfully detected. In the end, the future work to improve current approach has been discussed

Extracting Spatio-temporal Texture Signatures for Crowd Abnormality Detection

In order to achieve automatic prediction and warning of hazardous crowd behaviors, a Spatio-Temporal Volume (STV) analysis method is proposed in this research to detect crowd abnormality recorded in CCTV streams. The method starts from building STV models using video data. STV slices – called Spatio-Temporal Textures (STT) - can then be analyzed to detect crowded regions. After calculating the Gray Level Co-occurrence Matrix (GLCM) among those regions, abnormal crowd behavior can be identified, including panic behaviors and other behavioral patterns. In this research, the proposed STT signatures have been defined and experimented on benchmarking video databases. The proposed algorithm has shown a promising accuracy and efficiency for detecting crowd-based abnormal behaviors. It has been proved that the STT signatures are suitable descriptors for detecting certain crowd events, which provide an encouraging direction for real-time surveillance and video retrieval applications

An effective video processing pipeline for crowd pattern analysis

With the purpose of automatic detection of crowd patterns including abrupt and abnormal changes, a novel approach for extracting motion “textures” from dynamic Spatio-Temporal Volume (STV) blocks formulated by live video streams has been proposed. This paper starts from introducing the common approach for STV construction and corresponding Spatio-Temporal Texture (STT) extraction techniques. Next the crowd motion information contained within the random STT slices are evaluated based on the information entropy theory to cull the static background and noises occupying most of the STV spaces. A preprocessing step using Gabor filtering for improving the STT sampling efficiency and motion fidelity has been devised and tested. The technique has been applied on benchmarking video databases for proof-of-concept and performance evaluation. Preliminary results have shown encouraging outcomes and promising potentials for its real-world crowd monitoring and control applications

Brainwave Detection Model for Panic Attacks Based on Event-related Potential

Panic attacks could adversely affect a patient’s daily life and can pose risks to others. The symptoms of panic attacks can be timely observed by detecting the brainwave. This research presents a model that can evaluate the level of panic attack symptoms using the brainwaves detection during (or before) the symptom occurs. It helps monitor the patient’s brainwave based on Event-related potential (ERP). The model is derived from the simulation with horror pictures and frightening sound on the experimental group of 30 people. The survey related to symptoms has been used regarding to the criteria of the Beck Anxiety Inventory (BAI). The results showed that there is a consistent change of Electroencephalography (EEG) in each change of brainwaves where its quantitative analysis found that the changes of Beta, Gamma, and Alpha directly affect the model of Brainwaves Panic Attacks Measurement (BPAM) which is associated with panic attacks. 1 out of 30 cases scored higher than the average of the BPAM at 220 The Model BPAM can detect the risk to be Panic Attack compared to the use of tests Beck Anxiety Inventory (BAI) were found to be consistent. The test value BAI Score 19-63 was BPAM Score 401-1000. In addition, the results found that at P300 the brainwave pattern of EEG in meditation had decreased significantly whereas the brainwave related to attention had increased considerably for which human brain can potentially respond to stimulated external events

ANALISIS DAN DETEKSI GERAKAN KEPANIKAN MENGGUNAKAN METODE FRAME DIFFERENCE PADA SISTEM MONITORING RUMAH

Kepanikan, panik, gangguan panik atau serangan panik adalah semacam kecemasan dengan ciri diserang rasa takut yang luar biasa selama beberapa menit, timbulnya perasaan bahwa ada sesuatu akan terjadi, atau adanya ketidakmampuan untuk mengendalikan diri sekalipun sebenarnya tidak ada sesuatu yang buruk yang benar-benar terjadi. Seseorang dapat merasakan sensasi fisik yang kuat selama serangan panik berlangsung. Sensasi fisik itu mungkin terasa seperti berlari kencang atau mengalami serangan jantung. Sistem pengawasan saat ini menggunakan teknologi kamera. Suatu kebutuhan yang berkembang untuk pengawasan video yang lebih cerdas dari ruang pribadi atau publik menggunakan sistem penglihatan cerdas yang dapat membedakan apa yang secara semantik penting dalam arah pengamat manusia sebagai perilaku panik dan perilaku normal. Pada tugas akhir kali ini dirancang sistem untuk mendeteksi gerakan kepanikan berdasarkan kecepatan langkah kaki manusia. Metode yang digunakan untuk proses pengolahan citra dalam sistem adalah frame difference. Sistem ini dengan metode frame difference berhasil melakukan deteksi kepanikan berdasarkan kecepatan manusia pada sudut depresi 25o dengan menggunakan pencahayaan 100-250 Lux pada posisi gerakan lurus mendapatkan akurasi 71.43% dan gerakan tidak lurus mendapatkan akurasi 85.71%. Kata Kunci : Panic Detection, Frame Difference, Object Detectio