Flight dynamics modeling and recognition using finite state machine for automatic insect recognition
In this paper, we propose a novel state-based approach to insect flight dynamics modeling and automatic insect recognition (AIR). An emerging technology uses noninvasive laser sensors to capture flight information of flying insects in situ. However current computational entomology methods treat these optical signals as static patterns, rather than time series. We propose to evaluate each insect passage as a nonstationary process involving a sequence of pseudo-acoustic frames and model the short-term flight dynamics using a kernel adaptive autoregressive-moving-average (KAARMA) network. Since flight behavior is both nonlinear and nonstationary in nature, dynamic modeling provides a general framework that fully exploits the transitional and contextual information. Using the kernel adaptive ARMA algorithm and spatial clustering, each flight passage is defined to be an ordered sequence of states in a discretized spatiotemporal space. The state transition trajectories are used to construct a finite state machine (FSM) recognizer. The computational efficiency of the automaton classifier eliminates the need to perform numeric computations and enables realtime performance with far-reaching implications in entomology, agriculture, and disease control and prevention. Results show multiclass KAARMA classifier outperforms the state-of-the-art AIR methods, using support vector machine (SVM), deep learning autoencoder, and batch learning, in identifying Zika vector mosquito Aedes aegypti among five species of flying insects, while using significantly more efficient data representation. The extracted FSM from trained KAARMA network retained competitive performance compared to the current computationally expensive implementations.