# Abnormal Activity Detection Using Pyroelectric Infrared Sensors
## 2 Related Works  
Traditionally, cameras have been used for human activity classification and abnormal activity detection [14]. The processing of video includes background subtraction, human motion extraction and activity modeling [15]. The video data streams usually contain tens of thousands of pixels in each frame, and their intensity is easily effected by the change of illumination [5]. The excessive computational burden and the feeling of privacy intrusion make it difficult to be employed massively in real home environment. Wearable sensors is another paradigm for anomaly detection. [PERSON] _et al._[4] propose a two-stage approach for detecting abnormal activities. The first stage is to train an OSVM to model the commonly normal activities, and the second stage is to derive abnormal activity models from the suspicious activities filtered by the first stage. [PERSON] _et al._[2] propose using the wearable sensors together with location information provided by the camera to detect abnormal activities. A probabilistic framework is used to model different anomalies, including spatial anomalies, timing anomalies, duration anomalies and sequence anomalies. However, the biggest inconvenience of wearable sensing is that the sensors have to be recharged regularly even after deliberate design of power control units [2]. PIR sensing is a promising choice besides camera-based and wearable-based sensing. In [16], three PIR sensor models which are deployed in a hallway are used to detect the movement of eight human targets, including the two moving directions, three distance intervals and three speed levels. PIR sensor models can also be used to construct wireless sensor networks, which are intended to track and recognize multiple human targets [17]. Using only the binary information obtained by infrared sensors attached to the ceiling of a room, the human positions can be estimated, and even the number of humans in the room changes dynamically [18]. To capture the spatio-temporal feature of human movement, the monitored region is segmented into discrete sampling cells [19]. By leveraging the idea of compressive infrared sampling, the FOV of each PIR sensor is modulated by reference structure [9]. In [20], PIR sensors were used to extract the spatio-temporal feature of the human motion from the infrared radiation domain. Ten aerobic exercises, 360 examples in total, which were performed in front of the PIR sensor node were recorded, and the nearest neighbor classifier was then used to classify different exercises. Furthermore, they presented a PIR-based compressive classification method for recognizing six typical physical activities [21]. Similar to the experimental setup in [16], three sensor models were located on the ceiling, with opposite tripods facing each other. SVM and HMMs were used to evaluate the performance of their system [21]. PIR sensors are also used to detect abnormal activities, especially fall detection. In [22], PIR sensors were deployed in a distributed sensing paradigm, which aimed at capturing the synergistic motion patterns of head, upper-limb and lower-limb. The experiment results of fall detection were encouraging. However, their system was side-view, which means it was easily occluded by other objects, and the falls had to occur perpendicular to the FOV of the PIR sensors. In other words, it was view-dependent. To overcome these limitations, [PERSON] _et al._[3] proposed using the ceiling-mounted PIR sensor array to implement a fall detection system, _SensFall._ To achieve fall detection, the normal and abnormal training samples had to be collected beforehand. In other words, it was the supervised machine learning paradigm [23]. However, the abnormal detection is clearly a cost sensitive problem [4] because the samples of abnormal behavior were rare, or even non-existent. All types of the anomalies can not be elaborated on in advance. If we can only acquire the sensing data generated from normal activities, how can we train the system for abnormal activity detection automatically?