Track Initiation for Dim Small Moving Infrared Target Based on Spatial-Temporal Hypothesis Testing

Abstract

Track initiation for dim small moving target particularly in a heavy clutter environment is a theoretical and technological challenge for diverse tracking systems. The different spatial-temporal characteristics presenting in sequence scans are utilized to recognize target and initialize track in this paper. In spatial domain, the small target mapped in the image is a uniform gray spot other than pixel-sized object with high congregated degree, whereas, the false alarm is independent, irrelative and lower congregated degree. In temporal domain, the target's trajectory projected on image sequence is continuous for the continuity of target motion and will appear in the neighborhood at consecutive instants with the maximum probability, on the contrary, the false alarm is disorderly, and occurs in the neighborhood at consecutive instants is impossible. Based on the spatial-temporal characteristics mentioned above, a track initiation algorithm for dim small moving target based on spatial-temporal hypothesis testing, which consists of neighborhood clustering and trajectory continuity, is derived and analyzed in detail. The theory analysis and experimental results show that this method could effectively initialize the track for dim small moving target in heavy clutter environment.

Rectilinear-motion space inversion-based detection approach for infrared dim air targets with variable velocities

“…To validate the detection effectiveness of the proposed algorithm, we compare it with other four IR small target detection algorithms, including MNWTH algorithm, Zhang's algorithm, 15 Chen's LCM algorithm, 5 and Li's algorithm. 18 The comparison is shown in Fig. 17.…”

“…The representative sequential detection methods are temporal hypothesis testing, 7 sequential hypothesis testing, 8,9 Hough transform (HT), 10,11 three-dimensional (3-D) matched filtering, 12 and energy accumulation. [13][14][15][16][17][18] Temporal hypothesis testing 7 is like a classifier. The algorithm has achieved good performance since those temporal profiles of pixels through which targets pass will be distinct from those through which clutters pass.…”

“…However, just the velocity of the dim target is considered. Li et al 18 have proposed a detection algorithm based on neighborhood clustering and trajectory continuity. Nevertheless, the algorithm has just utilized the continuity of the target trajectory; the precise motion trajectory of dim targets is not considered.…”

Infrared small moving target detection algorithm based on joint spatio-temporal sparse recovery

“…In TBD strategy, the consecutive images are usually batch processed, and it could be represented as [14] Fðx; y; kÞ ¼ F t ðx; y; kÞ þ F b ðx; y; kÞ ð 5Þ…”

Sparse Representation for Infrared Dim Target Detection via a Discriminative Over-Complete Dictionary Learned Online

“…This causes great difficulty for infrared dim small target detection and tracking [1,2]. The problem of how to effectively distinguish dim small targets from clutter has been widely studied over the past years, and a number of dim target detection algorithms have been developed and they can approximately be classified into two categories, namely, detection before track (DBT) and track before detection (TBD) [3–5]. Image filtering and content learning are the two basic methods of DBT-based target detection algorithms.…”