# Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors
## 2 Related Work  
To avoid complicated data processing, especially feature extraction from the continuous vision sensor data stream [6], some researchers apply wearable or binary sensors instead for human daily activity recognition. [PERSON] et al. [16] proposed the simultaneously tracking and activity recognition of the occupants. Four kinds of binary sensors were employed to capture the human motion within each room. A dynamic Bayes net was used to infer the human location and achieve activity recognition by fusing heterogeneous sensor data. The Rao-Blackwellised particle filter (RBPF) was employed to solve the data association problem. [PERSON] et al. [11] integrated the location information and motion information to infer the daily human activity. An optical motion capture system was installed on the corner of the ceilings to provide the human location information. The target human body had an inertial sensor attached to capture the human motion. Neural networks were used to achieve coarse granularity activity classification, and hidden Markov networks were utilized to refine the fine granularity activity classification result. Finally, the location and motion information was fused based on Bayes' rule. Due to their simplicity and robustness to illumination variance, PIR sensors have recently been gaining increasing attention. In [17], [PERSON] et al. proposed the use of side-view-based PIR sensor nodes to locate human targets. Within its FOV, each sensor node can detect the angular displacement of a moving human target; multiple sensor nodes enhanced the localization accuracy. They applied the same hardware setting for multiple human tracking [18]. Their sensor nodes were deployed to facilitate the data association problem. An expectation-maximization-Bayesian tracking scheme was proposed to enhance the system performance. To avoid the region partitioning and region classifier, [PERSON] et al. [19] proposed a special optical cone to model the FOVs of the PIR sensors into petal shapes. Intersections of detection lines formed by these petal shapes defined the measurement points, which would be assigned credits to represent the probability of the human target falling within the FOV. The data association problem of multiple human targets can also be addressed by this credit-based method after cluster analyzing. However, in the research mentioned above, the PIR sensors were oriented in side-view or placed on the ground, which means that they were easily occluded by furniture or other obstacles in the real deployment. To overcome this drawback, [PERSON] et al. [20] attached binary infrared sensors to the ceiling of an office. Weak evidence such as people location, moving direction, and personal desks was synthesized to achieve soft tracking. They declared that their system can track up to eight persons with high accuracy. To increase the space resolution and improve the deployment efficiency of sensor nodes, [PERSON] et al. [21] applied another scheme of FOV modulation to realize human indoor localization. In their system, the FOVs of PIR sensors were modulated by two degrees of freedom (DOF) of spatial segmentation, which provided the flexible localization schema for information fusion. The Kalman filter and smoother were utilized to refine the human motion trajectory. Some researchers have been devoted to exploiting the potential of PIR sensors for activity recognition. In [22], [PERSON] et al. proposed the employment of pseudo-random code, based on compressive sensing, to modulate the FOVs of the PIR sensors. The human activity within a confined region was cast into low-dimensional data streams, and could be classified by the Hausdorff distance directly. [PERSON] et al. [23] proposed a method for abnormal behavior detection by investigating the temporal feature of the sensor data stream. Modified Kullback-Leibler (KL) divergence accompanied with self-tuning spectral clustering were leveraged to profile and cluster similar normal activities. Feature vectors were formed by hidden Markov models (HMMs).