{'fraction_non_alphanumeric': 0.018467852257181942, 'fraction_numerical': 0.0013679890560875513, 'mean_word_length': 5.416666666666667, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Short Local Trajectory based moving anomaly detection

A high level abstraction of the behavior a moving object can be obtained by analyzing its trajectory. However, traditional trajectories or tracklets are bound by the limitations of the underlying tracking algorithm used. In this paper, we propose a novel idea of detecting anomalous objects amid other moving objects in a video based on its short history. This history is defined as short local trajectory (SLT). The unique approach of generating SLTs from super-pixels belonging to a foreground object that incorporates both spatial and temporal information is the key in detection of anomaly. Additionally, the proposed trajectory extraction is robust across videos having different crowd density, occlusions, etc. Generally the trajectories of persons/objects moving at a particular region under usual conditions has certain fixed characteristics, thus we use Hidden Markov Model (HMM) for capturing the usual trajectory patterns during training. Whereas during detection, the proposed algorithm takes SLTs as observations for each super-pixel and measures its likelihood of being anomaly using the learned HMMs. Furthermore, we compute the spatial consistency measure for each SLT based on the neighboring trajectories. Thus, anomaly detected by the proposed approach is highly localized as demonstrated from the experiments conducted on two widely used anomaly datasets, namely UCSD Ped1 and UCSD Ped2.