The ability to visually locate and maintain targets is fundamental to modern autonomous systems. By augmenting a novel biologically-inspired target saliency estimator with Integrated Probability Data Association (IPDA) filters and linear prediction techniques, this paper demonstrates the reliable detection and tracking of small and weak-signature targets in cluttered environments. The saliency estimator performs an adaptive, spatio-temporal tone mapping and directional filtering that enhances local contrast and extracts consistent motion, strengthened by the IPDA mechanism which incrementally confirms targets and further removes stochastic false alarms. This joint technique was applied to mid-wave infra-red imagery of maritime scenes where heavy sea clutter distracts significantly from true vessels. Once initialised, the proposed method is shown to successfully maintain tracks of dim targets as small as 2×1 pixels with 100% accuracy and zero false positives. On average, this method scored a sensitivity of 93.2%, with 100% precision, which surpasses well-established techniques in the literature. These results are very encouraging, especially for applications that require no misses in highly cluttered environments.